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Using granular data on both debt and credit default swaps (CDS) exposures by French investors on non-financial corporations (NFC) and euro area banks on French NFCs, we study how CDS reallocate investors' exposure to credit risk. To guide our investigation, we propose a methodology to disentangle investors' strategies between speculators, hedgers, and arbitrageurs. We make three contributions. First, CDS reduce exposure concentration. Hedgers offset their most concentrated exposures while speculators use them as a substitute for debt. Second, speculators use CDS to reach for yield both between and within rating classes. This could pertain to relatively lower leverage constraints at lower ratings, or to the opacity advantage of CDS. Finally, CDS increase investment funds and dealers portfolio risk. Both reach for yield and exposure diversification contribute to this rise. Exposure diversification in the CDS market thus does not translate into return diversification.

Smart Contracts (SCs) are usually defined as contracts that are instantiated in computer-executable code that automatically executes all or parts of an agreement with the assistance of block-chain’s distributed trust technology. This is principally a technical description and results in an overly narrow focus. The goal of this paper is to provide an overview of the rapidly evolving multidisciplinary literature on Smart Contracts to provide a synthesis perspective on the economic implications of smart contracts. This necessitates casting a wider-net that ties SCs to the literature on the economics of AI and the earlier Industrial Organization literature to support speculation about the role of SCs in the evolution of AI and the organization of economic activity. Accomplishing this goal builds on a repurposing of the Internet hourglass model that puts SCs at the narrow waist between the real (non-digital) and virtual (digital) realms, serving as the connecting glue or portal by which AIs may play a larger role in controlling the organization of economic activity.

Litigant finance is a growing and increasingly controversial industry in which financial firms advance a plaintiff money in exchange for ownership rights in the proceeds of the legal claim on a nonrecourse basis: A plaintiff must repay the advance only if compensation is ultimately received for the legal claim. The nonrecourse nature of this funding exempts it from most states’ consumer credit laws, enabling funders to charge higher interest and fees than would otherwise be permitted. When this funding involves ordinary consumers, critics of the industry contend that the uncapped interest rates exploit vulnerable litigants, while its defenders argue that the availability of these cash advances improves the welfare of consumers, especially those who have no other credit options. This funding made headlines during the recent NFL concussion litigation, with more than one thousand players reported to have received such cash advances and with class counsel raising concerns of “predatory lending.” Because the industry has not been forthcoming with facts, the larger policy debate thus far has largely relied on anecdotes and speculation. In addition, the debate has ignored the important differences between pre- and post-settlement litigant funding. This Article is the first to present systematic, large-scale data on post-settlement litigant funding—the type of funding most NFL players reportedly received. We were given unrestricted access to the complete archive of sixteen years of funding applications and funding contracts from one of the largest consumer litigant funding companies in the United States. These data, which are robust and representative, enable us to make transparent the terms and true price to consumers of this formerly mysterious funding. We find that the Funder offers not only clearer contract terms but also better financial terms to post-settlement clients relative to pre-settlement clients. Yet these better terms do not come close to reflecting the virtually nonexistent litigation risk to the Funder. We therefore recommend that post-settlement litigant funding be subject to the same regulations as conventional consumer credit and that a standardized, simple disclosure be required.

The interwar gold standard is long thought to have prevented central bankers from running an independent monetary policy, forcing governments to leave this fixed exchange rate system in order to take control over domestic policy. But our study of the day-to-day management of monetary policy in the Netherlands reveals that officials were both willing and able to deviate from the monetary policy paths set by other countries, all while remaining firmly within the gold bloc. The Netherlands could afford to stay on gold thanks to its central bank’s plentiful gold reserves. Additionally, we find that central bankers could quell any speculation against the guilder by exploiting their domestic policy influence and international reputation to restrict capital mobility. However, remaining on gold until September 1936 came at a cost. Our international comparisons and counterfactual simulations suggest that Dutch policymakers could have avoided a deepening the Great Depression by leaving gold alongside the UK in 1931. Our research opens new perspectives on the golden fetters literature, and revises the historiography on Dutch interwar monetary policy, by describing the operationalisation of the gold standard in the 1930s, and quantifying the costs of continued gold standard adherence.

A large segment of cryptoassets consists of tokens—simple code running on programmable blockchains such as Ethereum—that serve as a payment or governance mechanism for an underlying digital platform, usually a peer-to-peer marketplace of various services. An ongoing debate about the viability of the token market is centered on whether tokens are used purely for speculation, or have actual utility on their underlying platforms. The objective of this study is to create and validate a set of granular measures of token utility and speculation. Using disaggregated data on 270 million transactions in 891 tokens hosted on the Ethereum blockchain, we construct three intuitive measures of token utility and speculation: utility turnover (UT), speculative turnover (ST), and DeFi ratio (DF). These measures first reveal a striking decline—of about 90%—in average token utility usage since 2017. This decline coincides with an equally pronounced increase in token speculation, with exchange-based speculation peaking at mid-2020. We show that the emergence of DeFi applications, which offer increasingly levered and complex speculative tools, are crowding out both utility and speculation usages and further extending the increase in token speculation. We provide additional economic validation of our measures by relating them to future market outcomes and show that token utility negatively predicts return volatility in the cross section, while both speculative measures positively predict volatility. Additionally, DeFi activities positively predict future market-adjusted returns. These results suggest that our measures embed value-relevant information about the economic activities in the token-linked platforms, and about the intensity of speculative noise trading in these tokens, thus making them useful outcome or explanatory variables in future empirical analyses of blockchain tokens and platforms.

It is widely agreed that the Nasdaq during the dot-com era 20 years ago was a full-fledged stock market bubble. Recently, the US stock market according to many metrics has become significantly more speculative and overvalued than it was at the dot-com peak 20 years ago. In both instances, a very broad subset of stocks became so highly valued that speculation in them had to be untethered from all fundamentals: the essence of what we call a “pure price-chasing bubble.” This paper, drawn from a book in progress, examines the history of stock markets for comparable pure price-chasing bubbles, finding nine or so which have ever reached such a speculative extreme, with an over-the-counter market in Kuwait in the early 1980s called the “Souk al-Manakh” representing the most extreme example. Based on my personal exposure to this Souk al-Manakh almost 40 years ago, I describe this anatomy and thereby make transparent the recurrent dynamics—on the way up and on the way down—of these greatest asset bubbles in human history. When one applies this framework to the current US stock market, one sees that the stock market in the US today will likely follow the disastrous path of the dot-com market.

The speciality of determining stock prices has been a troublesome task for many researchers and examiners. Indeed, financial specialists are profoundly intrigued by the examination region of stock value prediction. For decent and useful speculation, numerous speculators are sharp in knowing the stock market's future circumstance. Tremendous and powerful prediction frameworks for stock market help dealers, speculators, and experts give vital data like the stock market's future heading. This work presents a recurrent neural network (RNN) and Long Short-Term Memory (LSTM) way to deal with anticipated stock market files.

The article 'Entrepreneurial Incentives and the Role of Initial Coin Offerings' by Garratt and van Oordt revisits a classical principal-agent problem in corporate finance: How do financing choices of firms and the according firm ownership structure impact effort choices of the management and therefore firm value? Garratt and Oordt extend this classic discussion by analyzing initial coin offerings (ICOs) as a startup financing instrument in contrast to classic financing instruments such as debt, (entrepreneurial) equity and venture capital. Their paper shows, depending on the firm's characteristics, ICO financing can align the entrepreneur's incentives with those of his investors better or worse, in comparison to venture capital and debt financing. Therefore, the novel ICO financing instrument is a valuable alternative to credit and venture capital also from a social perspective. I find the efficiency comparison of financing choices for entrepreneurial incentives to be a very interesting and important contribution. In this discussion, I shed light on this topic from a different angle. Garratt and van Oordt base their analysis on the simplifying assumption that demand for the platform product realizes once and for all in t=1. This allows Garratt and van Oordt to abstract from token-exchange rate risk. Here, instead, I will allow platform demand to evolve stochastically across time. Moreover, unlike in Garratt and van Oordt, I analyze the general case where not necessarily all tokens are sold in the market at every point in time. Instead, there can exist token speculation where investors hold on to tokens for a certain period of time, effectively reducing the token supply, or additional tokens can be mined by the entrepreneur, which increases the token supply, even though the latter activity might constitute a breach of contract. By allowing the token supply and product demand to fluctuate across time, the token exchange rate becomes stochastic, causing exchange rate risk to the ICO investors, the entrepreneur, and platform retailers.

We study the effects of quantitative equity investing, an increasingly popular investment style, on financial market quality. Within a noisy REE model of strategic speculation with two informed market participants, we define discretionary investing as fully strategic trading and quantitative investing as partially or fully myopic via its reliance on a backtested trading strategy. Growth in quantitative investing is modeled as both the introduction of and the greater backtest adherence by an informed speculator. The introduction of an additional speculator generally benefits financial market quality. The effect of greater backtest adherence depends on whether it leads to more or less aggressive trading than discretion, the former improving, while the latter worsening market quality. If it is more aggressive, market quality broadly benefits with greater quantitative investing; if it is less aggressive, market quality deteriorates.

Cryptocurrencies have gone a long way since their inception. While the conventional financial sector initially dismissed digital currencies as tools for crooks and speculators, the sector has made considerable progress in establishing itself as a genuine and (possibly) world-changing arena. There are still concerns about the long-term effects of widespread cryptocurrency usage. Many skeptics and environmentalists, in particular, have expressed worry about the energy consumption of cryptocurrency mining, which may result in increasing carbon emissions and climate change. The mainstreaming of cryptocurrency, as it has been dubbed, is clearly a significant event in the world of finance. It's also a significant event in the world of, well, the globe. Whether you like cryptocurrencies or oppose them, there's no denying that bitcoin and other proof-of-work blockchains use massive amounts of energy.

I review the literature on financial speculation driven by belief disagreements from a macroeconomics perspective. To highlight unifying themes, I develop a stylized macroeconomic model that embeds several mechanisms. With short-selling constraints, speculation can generate overvaluation and speculative bubbles. Leverage can substantially inflate speculative bubbles and leverage limits depend on perceived downside risks. Shifts in beliefs about downside tail scenarios can explain the emergence and the collapse of leveraged speculative bubbles. Speculative bubbles are related to rational bubbles, but they match better the empirical evidence on the predictability of asset returns. Even without short-selling constraints, speculation induces procyclical asset valuation. When speculation affects the price of aggregate assets, it also influences macroeconomic outcomes such as aggregate consumption, investment, and output. Speculation in the boom years reduces asset prices, aggregate demand, and output in the subsequent recession. Macroprudential policies that restrict speculation in the boom can improve macroeconomic stability and social welfare.

We examine the association between margin requirements and the market’s efficiency in incorporating firm-specific and market-level public news. Combining the Fed’s 22 changes in margin requirements with a hand-collected sample of earnings announcements between 1934-1975, we show that higher margin requirements induce greater delay in incorporating earnings information into prices. We draw similar conclusions when we analyze the Hou and Moskowitz (2005) price delay measure, as well as indirect measures of leverage constraints over recent years. Further tests suggest that, despite the Fed’s expressed intent to curtail excess speculation, higher margin requirements restrict trading by arbitrageurs more than noise traders.

Vaccine mandates have historically been implemented and regulated by the states, not the federal government .Nevertheless, the federal government has “broad, flexible power” to prevent the spread of communicable disease among the states through Section 361 of the Public Health Service Act (Section 361). Although the federal government has never used Section 361 to mandate a vaccine to prevent the spread of disease and the authority to mandate a vaccine is not expressly provided in the statute, there is speculation that the federal government could use Section 361 to mandate a vaccine to prevent the spread of disease. However, there is no authoritative interpretation of the scope of the powers in Section 361, and the text remains ambiguous. This Comment sets aside the question of whether the federal government should mandate a vaccine and instead explores whether the federal government could mandate a vaccine through Section 361. Ultimately, this Comment concludes that Section 361 allows for a federal vaccine mandate and recommends a framework for determining when a federal vaccine mandate is a reasonable and necessary measure.

In recent years, there emerge some new financing techniques as additional financing options for the entrepreneurs, especially for those, such as start-ups, that ordinarily faced difficulties in accessing the traditional financing techniques. Among these new techniques, reward-based crowdfunding (crowdfunding) and the initial coin offerings (ICOs) are the two most popular strategies. In this paper, we build a stylized model to investigate how the product characteristics affect the entrepreneur's financing choice between crowdfunding and ICO. We show that ICO (resp. crowdfunding) is more suitable for intangible (resp. tangible) products that typically have high (resp. low) fixed costs and low (resp. high) production costs. This result is built off of their different fundraising mechanisms. Under crowdfunding (resp. ICOs), the fundraising is through sacrificing the profit in the funding stage (resp. both funding and market stages). Moreover, speculators can be a double-edged sword to ICOs. On the one hand, the participation of speculators can increase the financing success rate of ICOs. On the other hand, the competition from speculators in the token transaction market will hurt the entrepreneur. We also show that ICOs become more preferable than crowdfunding in the presence of the R\&D failure risk. Finally, we examine the setting with endogenous product quality and show that ICOs lead to higher product quality than crowdfunding when the startup cost is low, or when either of the platform traffic and the production cost is high.

This topic has been chosen to investigate the effect of the corporate life cycle on the firm's performance. There are ten IT companies listed on the Dhaka Stock Exchange (DSE). This study investigates several financial years’ performance of 10 IT Companies listed in DSE. We find that how the stages of the life cycle vary over the firm's performance. Several variables of the corporate life cycle are used such as introduction, growth, mature, shake out, and decline which are used to measure the return on asset for the IT Company. Measuring company performance is a very important task to survive in the market. The profitability can pass through totally different stages during its life cycle. The profitability can pass through totally different stages during its life cycle. The main purpose of this study was to spot the variables that affect the profitability or firm’s performance of DSE listed IT Company. In line with the speculation of the life cycle, the characteristics of all variables of a company affected by the stage of the corporate life cycle that firms are set in it. There’s stagnation in growth at this point. The profitability could stay during this state for several years solely to decline and dissolve eventually. However, firms handle this stage of the corporate life cycle can confirm what proportion longer they need to be left within the field. My results show that the profit of those firms increased at the intro and growth level, and later at the maturity level, the profit decreased a small amount once more. It might be some reason that the growth stage of the life cycle of firms wherever firms have taken several types of initiatives like multiplied new product channels, or they were significantly proactive, or they were purposeful. The second chapter of this paper is the Literature Review, which provides detailed related information and documented pieces of evidence from previous studies. The third chapter outlines the overall research methodology including data collection and sample size description and sample, sampling technique, data sources, and data analysis technique of the study. Data have been collected from the annual report of the Dhaka Stock Exchange-listed ten IT Companies. The fourth chapter has examined the findings of this study in an appropriate table and data analysis comprises a decent heading and description. This study has identified several factors from the corporate life cycle measurement that all the variables influence company profitability. Some of these factors greatly influence firm performance and some factors have great importance and some others have moderate importance on company decision-making. Finally, we have presented with the overall situation of the corporate life cycle stages of our chosen company, and the improvement of the study.

The role of cryptocurrencies as a vehicle for speculation has been well established. However, it is less clear if cryptocurrencies can also serve to manage risk. The authors seek to determine the diversification potential of cryptocurrencies both for short and long horizons. For short horizons, they estimate correlations that consider the direction and magnitude of returns for relevant asset classes, rather than focus on full-sample correlations, as is customary. For long horizons, they compute “single period correlations,” which capture the extent to which cryptocurrencies move synchronously or drift apart from other assets over an investor’s horizon. They also identify utility-maximizing allocations to cryptocurrencies directly from historical return samples that account for all features of the data as well as more nuanced preferences than typically assumed.

This paper exploits a regulatory experiment that lifts the margin trading ban in China to examine the real effects of speculative retail investors. Using a regression discontinuity design, I find that margin trading eligibility causes an increase in share turnover and stock prices, and that marginable firms cater to investor short-termism by manipulating earnings and reducing long-term investment. Consistent with managerial myopia, marginable firms experience a decline in operating profit and equity valuation in the long run. My results suggest that margin traders, as short-term speculators, pressure the manager to focus on current earnings and sacrifice long-term growth.

Clientele-based theories explaining asset price bubbles are often difficult to test because the identities of investors cannot easily be tracked over time. This paper tests these theories using a hand-collected sample of 12,000 investors during an asset price reversal in the shares of British bicycle companies between 1895 and 1900. We find that informed investors reduced their holdings substantially during the crash, suggesting that they were riding the bubble. Those who performed worst were not typically the least informed groups, but gentlemen living near a stock exchange, who had the most time, money, and opportunity to engage in speculation.

Speculative economic, financial, and cryptocurrency bubbles are not arcane anymore; nonetheless, they are still misunderstood. For this exact reason, they continue to form even centuries after the famous first speculative bubbles of 17th and 18th centuries. Bubbles do not form instantaneously; quite the opposite, they progress through several distinct stages that can be monitored and studied in order to take proper policy actions to avoid costly crises. Speculation, as an economic cycle, fuels investment activity; therefore, it is not entirely bad unless it is done excessively via manipulative actions which ultimately cause panic among investors. Speculation alone does not result in a crash, but the induced fear spiraling through the broader economy like the venom of a poisonous snake can be enough to rattle markets and cause bubbles to burst. Exactly what happened in the famous first three bubbles; the Dutch Tulipmania (1634-38), the Mississippi Bubble (1719-20), and the South Sea Bubble (1720). We all know that history repeats itself; every time it does, the damage is far greater than before. Three centuries after the famous first bubbles, the 21st century began with its own famous three bubbles; the Internet Bubble (the dot.com crisis of 2002), the U.S. Housing Bubble (the subprime crisis of 2006-07 followed by the 2008 global financial crisis), and the Cryptocurrency Bubble (Bitcoinmania).

As of June 12, 2021, the end of the COVID-19 pandemic appeared to be fast approaching. As such, there is now increased debate with respect to the nature of a post-COVID economic recovery. Several financial writers have referenced the Roaring 20s as a historical period that may provide useful lessons. However, a deconstruction of the key economic drivers during the Roaring 20s suggests that this period provides few parallels to the circumstances in 2021. There are two historical periods, however, that do offer useful comparisons: the years 1919 and 1999. Drawing from the lessons from these two years, it seems plausible that a post-COVID recovery may combine a consumer spending rebound that is reminiscent of 1919 with increased speculation in the stock market that is reminiscent of 1999. The paper concludes by encouraging investors to exercise caution, noting that neither the spending rebound in 1919 nor the speculative stock market bubble of 1999 lasted long. Therefore, it seems conceivable that after the initial, post-pandemic euphoria wears off, a similarly painful economic contraction and market correction could soon follow. Should this scenario occur, readers may benefit from considering this possibility and preparing psychologically for the consequences.

In this paper, we examine how the enforcement and collapse of a rumor affect regional housing markets in China. Specifically, we trace the rumor that Yanjiao and Dachang, two counties in Hebei province, were to be merged into Tongzhou, a county of Beijing, and quantify the bust of housing prices when the rumor collapses. Using a difference-in-difference approach in a sample of housing units listed between 2015 -2019, we find that listing price in Yanjiao and Dachang drops by 25.1% relative to Tongzhou since local governments clarify the rumor. We further evidence that herding and speculation accelerate the price bust, and a small group of housing speculators trade the price down through the contagion effect.

This paper considers a monopolist firm selling two collections of a single product sequentially to satisfy consumers' different social externalities. The first "capsule" collection has a unique design and is sold within a limited time frame. The second "mass" collection has the same functional features except for the appearance. Both snobs who pursue exclusivity and conformists who desire conformity are present in the market. Speculators, who have no consumption value for the product, enter the market to speculate quick money by reselling the purchased capsule collection on the resale market. All three groups of buyers are forward-looking. We examine the monopolist's pricing and production decisions at the beginning of each period and the buyers' behavior in each market. We find only snobs and speculators are willing to purchase the capsule collection. In the selling season of the mass collection, a large proportion of conformists lead to an upward-sloping demand curve, which allows the monopolist to use a "static" pricing scheme for both collections. Even though the monopolist can price the speculator out, she sets a lower price and releases more capsule products to foster the resale market. It can rivet the snobs with the capsule collection and reduces their protective effects on the law of demand for the mass collection. The resale market's presence also triggers more need for the mass collection by social influence and increases consumers' valuation of the product. Finally, we identify the conditions under which the monopolist prefers static pricing to dynamic pricing.

Meme stocks have received a lot of attention in the media from both investors and regulators in recent months. The power of the crowd, coupled with the unprecedented coordination of social media investors, raises questions about their impact on information efficiency. We construct two meme stock indices based on stocks whose purchase the Robinhood app restricted during the GameStop episode. We provide evidence of meme stock market efficiency. Our results indicate, perhaps contrary to some early speculation, that since early 2020, this new influx of highly connected retail investors improves market efficiency.

This paper looked at accuracy in market price and real-time updates of chart through computer usage to the inclusive financial market world. It also examined the revolution in the advancement of technology based on the application of computer in the financial market world. The evolution of computer devices has created an open opportunity for more and more participants in the financial market. The profitability of investments in financial markets highly depends on the predictability of price movements. If a forecasting model or technique can precisely predict the direction of the market, investment risk and uncertainty can be minimized. Market execution are instant which now produces immerse gain to the traders, buying, selling and holding currencies in order to make a profit from favorable fluctuations in exchange rates. Trading The forex market is one of the most popular markets for speculation, due to its enormous size, liquidity, and tendency for currencies to move in strong trends.

The status of the City of London as a global financial center has been at the heart of much speculation over the past five years, following the outcome of the Brexit referendum. One of the key questions that has been debated in both media and academic outlets is related to the capacity of the City to attract business, especially once the rights flowing from the EU Passport system cease to be in place (1st January 2021). Effectively, the new regulatory scenario emerging from the post-Brexit world is likely to put the City of London in more direct competition vis-a-vis other European markets. Questions of competition have come to the fore more recently, in March 2021, with the Review of the UK listing regime, produced by Lord Hill. This deals with aspects of UK financial regulation, and more specifically with the UK listing process and its effectiveness in attracting companies, both domestic and overseas. It is fair to say that, together with investor protection concerns, the Review has a distinctive competitive objective, which transpires from its focus. It is also evident that the implementation of this Review would cause a departure of the UK from a number of EU legal and regulatory standards.

The diffusion of social media coincided with a worsening of mental health conditions among adolescents and young adults in the United States, giving rise to speculation that social media might be detrimental to mental health. In this paper, we provide the first quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. Our analysis couples data on student mental health around the years of Facebook's expansion with a generalized difference-in-differences empirical strategy. We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression, and led to increased utilization of mental healthcare services. We also find that, according to the students' reports, the decline in mental health translated into worse academic performance. Additional evidence on mechanisms suggests the results are due to Facebook fostering unfavorable social comparisons.

14 January 2020: After months of speculation, Google finally announced its plan to phase out third-party cookies on Chrome within the next two years. As Chrome boasts a worldwide market share in excess of 64%, the news sent shockwaves in the online advertising industry, whose foundations are largely built on web cookies. Concerns have been expressed that the banning of third-party cookies from Chrome could further strengthen Google and Facebook because of their large addressable first-party audience at scale. As to Google’s proposed “Privacy Sandbox” initiative, which it claims will “fundamentally enhance privacy on the web”, no one really knows how it will be implemented in practice and the industry seems to be confused. This uncertainty could hurt investment in an area that has already lost its attractiveness to investors. Against this background, this short paper examines Chrome’s policy change and its wider implications on the online advertising industry. Yet in order to appreciate the importance of the announced change we need first to get back to the basics and understand what cookies are and how they are used in online advertising. This is all the more the case considering that, while many people talk about “cookies”, few seem to have a solid understanding of what they are, how they are created, and for which purpose.

A narrative that the largest companies in public markets (aka the Big Five - Facebook, Amazon, Apple, Microsoft and Google) were at one point all VC-backed unicorn startups and achieved their success by "blitzscaling" has propagated a belief that money-losing startups pursuing growth at any cost can offer investors a chance to replicate the Big Five’s outsized returns. In recent years this narrative has led to an explosion in unicorn startups, alongside increasing capital inflows entering into private late-stage startups as a whole. New sources of capital flooding into private late-stage startup fundraising deals comes not just from VC funds but from “non-traditional” VCs, a group that includes mutual funds, hedge funds, sovereign wealth funds and corporations. Historically non-traditional VC investors focused more on stock market investing, but in recent years this group has increasingly participated directly in private market fundraising rounds of late stage startups as a strategy to boost reported returns. In doing so these investors have adopted the VC mindset for investment analysis, prioritizing near-term sales growth and potential for market dominance over profitability. This influx of capital has created an attractive environment for late stage startup fundraising, which has been a contributing factor to elevated valuation levels observed in the space by virtue of the common (but misleading) convention of substituting post-money value for fair value used by private market investors. This upward skew in valuations is a source of bias in the top line returns reported across this asset class, which further reinforces capital inflows to the space. The consequence of the collective dynamics described above has been a positive feedback loop in valuations for late-stage VC-backed startups in private financial markets. In the last few years, VC-backed IPOs have been growing in frequency and size as public market investors are succumbing to price-driven narratives that rely heavily on VC-style investment analysis and less on traditional valuation methods based on normalized earnings and cash flows. This has created the situation where equity valuations for late-stage VC-backed startups have been bid up by speculation and have decoupled from their underlying business value.

Cloud computing gives a support to purchasers to lessen their internal establishment, and for providers to extend wages, using their own specific system. The best possible burden adjusting and dynamic asset provisioning improves cloud execution and draws in the cloud clients. In this paper, we propose a mechanized asset provisioning calculation, Speculation asset provisioning, inciting load adjusting through theoretical methodology in asset provisioning. As an endeavor to measure asset portion we utilize two level versatile expectation component to check the computational examples of past asset distribution to the future necessity. The structure ensures reasonable assets required for the application, by avoiding over or under-provisioning of asset and supports vitality effectiveness in asset distribution. We use estimation system to address the fluctuation in the verifiable information to adjust the theory overhead. We have passed on our proposed work in an open source cloud structure and differentiated our results and other AI draws near. Our Experimental outcomes show versatile asset designation over client driven help the executives under the quickly changing necessities of cloud computing.

To explore how speculative trading influences prices in financial markets, we conduct a laboratory market experiment with speculating investors (who do not collect dividends and trade only for capital gains) and dividend-collecting investors. Moreover, we operate markets at two different levels of money supply. We find that in phases with only speculating investors present (i) price deviations from fundamentals are larger; (ii) prices are more volatile; (iii) mispricing increases with the number of transfers until maturity; and (iv) speculative trading pushes prices upward (downward) when the supply of money is high (low). These results suggest that controlling the money supply can help to stabilize asset prices.

This paper examines how technological innovations drive fast trading investment for both speculators and exchanges and their impact on market. The negative externality of the speed acquisition from fast speculators can result in excessive investment, which is intensified as speculators’ speed technology advances. As exchange’s speed technology advances, faster exchange makes faster speculators more concentrated; that is, higher exchange speed shrinks market fraction of fast speculators but stimulates their optimal trading speed. As the result, market liquidity is improved but price discovery is reduced. Policy makers aiming to balance price discovery and deadweight loss from costly speed investment may lead to a mismatch between the desired exchange speed for policy makers and the optimal speed supplied by exchange, echoing the concerns of market regulations about market failure on speed arms race.

This paper uses two similar local jurisdiction merger policies at different phases of a housing cycle to examine whether home purchasers form their expectations of future house prices based on lagged price changes. Using a detailed housing transaction data set, we find that, during the boom period, the relative home prices in the merged area increased significantly for a few months after a positive fundamental shock and then reverted. However, during the bust period, we find no overreaction of the relative house prices in the local merged districts after a similar shock. Moreover, we find that short-term speculators may be the main contributor to the housing price overreaction. Overall, our results confirm the role of over-extrapolation in house price dynamics.

The level of alcohol consumption and the role of alcohol taxes as a source of state revenue in pre-Petrine Russia have been the subject of much scholarly interest and political speculation, yet reliable data on the magnitude of trade in “grain wine” is still lacking. This article draws on the records of central government agencies from the 1620s - early 1650s to partially fill this gap. In the decades immediately following the Times of Trouble tavern revenues nearly doubled in absolute numbers and accounted for about a quarter of the total state revenues of Muscovy. The growth rate of tavern revenues was on par with the rate of population growth (between the 1620s and 1640s, urban population increased by 60 percent). The article discusses different methods of running the state alcohol monopoly and estimates the profitability of alcohol trade and the overall levels of alcohol consumption during the period under study.

This study investigates the spillover effect of corporate headquarters relocation on the local housing market. Using a comprehensive database to identify corporate relocation events in the U.S. from 1994 to 2017, we show that moving a corporate headquarters into a district is significantly associated with housing price appreciation in the district. The growth in housing prices for districts with a corporate headquarters relocation is 10% higher than in those without. Moreover, housing prices increase in the year before the relocation, which suggests the presence of a speculation and expectation effect. Housing prices increase further until 2 years after the relocation, which demonstrates the prolonged impact of corporate relocation on the housing market. The cumulative effect over the period results in more than 30% higher growth in housing prices for districts with a corporate relocation. Further, we find a significant spatial spillover effect of corporate relocation on nearby districts’ housing markets up to 15 miles. We next examine the channels through which corporate relocation affects housing prices, including the relocating firm’s employee base, firm activity, and agglomeration economies. Lastly, we observe a marginally negative effect as a result of a headquarters moving out of a district. Overall, our study indicates that corporate relocation exhibits significant positive spillover on the local housing market.

This paper investigates the impact of speculative trading on the commodity futures risk premium. We focus on speculators’ spread positions, and study the asset pricing implications of spreading pressure on the cross-section of commodity futures returns. In an era of financialization of commodity markets, a long-short portfolio based on the spreading pressure signal carries a significant risk premium. We show that spreading pressure reflects speculators’ expectations about the change in the shape of the futures term structure, which is linked to commodity index investment. The spreading pressure factor can be explained by economic fundamentals and frictions introduced by financial traders.

Most research on the run-up in home prices before the Great Recession has focused on types of excessive demand—loose lending, foreign savings, loose monetary policy, speculation, bank deregulation, federal housing subsidies, etc. The focus on excess demand led to fatalism about the collapse in homebuilding that began in 2006 and the eventual recession and financial crisis that followed. Regardless of the sources of excess demand, a consensus developed that American spending had become unsustainably high because of a housing bubble and that demand needed to be reduced. Using a broad array of evidence, we find that constrained supply of new housing in key urban centers was the primary trigger for high home prices before the crisis. The recession and the financial crisis were the result of deliberate contractions of demand—both generally and specifically in residential investment—that were neither useful nor necessary. First, tightening monetary policy reduced aggregate spending and encouraged negative sentiment about real estate. Then, policy changes tightened lending standards and depressed housing markets for years after the 2008 financial crisis. A monetary policy regime targeting stable nominal income growth would have dramatically improved the economy during and after 2008.

We report new evidence that speculation in energy and precious metal futures are more prevalent in crisis periods and even more so during the COVID-19 pandemic. In contrast, agricultural futures attract more hedging pressure. Post-GFC patterns mirror the 1980s’ recessions. Using quantile regression on a long-horizon sample we also find that speculative pressure generally coincides with abnormal returns in normal circumstances but not in the current pandemic. Instead, volatility is strongly and often non-linearly associated with speculation across instruments.

All corporate valuation models rely on very long forecasts of free cash flows. The only question is whether those forecasts are accounted for explicitly by using an extended valuation model or implicitly in an estimate of the terminal value after an explicit short-term forecast period of five to ten years. Given current computing technology, there are good reasons to use projections running out multiple decades. Doing so gives a clearer picture of the long-run issues that affect a company's value. Of course, developing very long-term forecasts is difficult and may be considered speculative, but the difficulty and speculation are not removed by assuming that at a horizon of five or ten years the firm enters steady state and applying a constant growth terminal value model. A better approach in many circumstances may be to explicitly take account of the need for very long-term forecasts, raising the question: “Is it time to terminate the traditional terminal value?”

We investigate the co-movement of stock prices and intrinsic value estimates focusing on the estimation of risk. We apply risk measurements based on a) market and b) accounting data. We find that price and value co-move from 1983 to 2014 on an index-level using accounting-based risk measurement in contrast to the market-based risk measurement. Our findings have two vital implications. First, the lack of co-movement documented in prior research can alternatively be explained by the applied valuation model (market vs. accounting), rather than investors trading behavior (e.g., speculation). Second this result provides strong empirical evidence that accounting information is useful for equity investors. Finally, we analyze the role of accounting conservatism regarding co-movement. We document that on an index level conservative reporting harms the co-movement of price and value. However, conditioning on a co-movement of price and value, more conservatism is helpful for moving prices back to fundamentals.

We propose that speculative trading arising from the joint effect of investor disagreement and short-sale constraints plays an important role in explaining the idiosyncratic volatility (IVOL) puzzle, the correlation among IVOL, market beta and trading volume, and the co-movement of IVOL. Empirical tests show that the return spread between high and low IVOL quintile portfolios is closely related to both aggregate and firm-level disagreement. The common IVOL (CIV) factor is strongly correlated with the aggregate disagreement. The correlation between the IVOL effect and disagreement presents mainly among stocks that are more likely to be short-sales constrained. We provide a mechanism that high aggregate disagreement can lead to more firm-level speculative trading, which is consistent with a factor structure in IVOL and positive correlation among stock beta, the IVOL effect, and the trading volume.

This review will examine the implementation of artificial intelligence that has been taking place within the banking industry. There have been many opinions about artificial intelligence (AI) and its ability to make things more efficient across multiple industries. The banking industry is no different. There has also been speculation that artificial intelligence may have a negative effect on the banking industry as well. This literature review will discuss further in the following discussions and research.

This is the first paper that explores lottery-like demand in cryptocurrency markets. Since recent research provides evidence that cryptocurrency returns are rather short-memory processes in their nature, we modify Bali et al.’s (2011, 2017) MAX measure and employ a weekly forecast horizon and last week’s daily log-returns for calculating the metric for our portfolio sorts. From an econometric point of view, this study proposes statistical tests that are robust to unknown dynamic dependency structures in the cryptocurrency data. Our results show that average raw and risk adjusted return differences between cryptocurrencies in the lowest and highest MAX deciles exceed 1.50% per week. These results are robust to controls for Bitcoin risk or potential microstructure effects. Our findings are important also from a theoretical point of view because they suggest that parallel to the stock markets, similar behavioral mechanisms of underlying investor behavior are present also in new virtual currency markets.

Nevile writes that : “Thus, it is of considerable interest to speculate on what Keynes would have thought about the way ISLM became identified as "Keynesian" economics for most of the economics profession. We know, of course, what his first reaction to ISLM was in 1937.”(Nevile,2000,p.133). Nevile is simply ignorant of the fact that Keynes spent pp.298-303 of Chapter 21 of the General Theory in 1936 first presenting the model on pp.298-299 and then discussing his IS-LM model’s strengths and weaknesses on pp.300-303.There is thus no need whatsoever to engage in any speculation about some supposed reactions on Keynes’s part in 1937 since Keynes’s “reactions” are all laid out explicitly in 1936 on pp.298-303. Nevile’s 2000 view is simply repeated in 2016(2002) in Kriesler and Nevile: “In Section 2 we attempt to identify the ‘essence’ of Keynes’s central message and in Section 3 examine Keynes’s reaction to various formulations of the IS-LM to see what he thought important if an IS-LM framework was to be a good summary of the General Theory. We then consider whether Hicks’s IS-LM framework was an important step in the eventual distortion of Keynes’s message.”(Kriesler and Nevile,(2016),pp.69-70). We reach the same conclusion as we reached above on Nevile’s article ,since Keynes explicitly discusses what he viewed as the strengths/deficiencies of the IS-LM model in detail in 1936in chapter 21.It is not necessary to scrutinize Keynes’s 1937 replies to Hicks and Harrod,whose work represents half baked versions of Keynes’s superior formulation of IS -LM that is built on the D-Z model of chapter 20. Keynes’s discussions of the deficiencies in IS-LM DOES NOT include questions about expectations and uncertainty, since both expectations and uncertainty are incorporated in the D-Z foundations of the IS-LM model,with the relevant D-Z combination being linked to a particular Y value that is then combined with r to specify (r,Y) space ,where the IS-LM model is then specified by Keynes. No economist has ever created an IS-LM model that rivals Keynes’s,since all other IS-LM approaches fail to explicitly cover and incorporate expectations and uncertainty with one exception-Champernowne’s 1936 paper.

The Golden Age of science fiction - particularly the 1940s and 1950s - witnessed a number of technologically oriented novels and short stories by the most famous authors, Isaac Asimov, Arthur C. Clarke, and Robert A. Heinlein. A common hallmark in these volumes is speculation on devices for calculation in the far future. This article describes the instruments found in their signature works and their relation to the mathematical (or "goofball" pseudo-mathematical) tasks that drive the narratives. A recurring theme is that the writers' vision of the future depended on the technology of their own day. A novel about the far future Galactic Empire might refer to microfilm, a bold development for 1940 but already obsolete in our time. The article concludes with observations on the continuing relevance of calculation in speculative fiction of our time and our future.

The strategic case for gold revolves around its long history as a store of value. Humans have been drawn to the yellow metal despite its inability to pay a dividend or provide productive output ownership since somebody first discovered it. From an investment perspective, gold has demonstrated an ability to provide a long-term source of uncorrelated returns while also serving as something of a "flight to safety" hedge during geopolitical or fiat instability periods. While there are plenty of speculators looking to gold as a source of potential outsized return, most of the investing public looks to gold at the intersection of those two objectives: risk management. The authors make the case that there is a risk premium for investing in gold, understanding that even though gold does not provide a stream of future cash flows paid out to investors, it has genuine utility as a store of value. The authors also explore the benefits of managing gold exposure in investment portfolios to manage large drawdowns through systematic momentum measures.

Imposing a property tax is a common measure that governments take to regulate speculative real estate activities; however, its effectiveness in cooling market prices is not clear. Starting in 2010, the Hong Kong government introduced a series of Special Stamp Duty (SSD) policies to curb property flippers. We find that although the SSD effectively curtailed flipping transactions from 21.1% in 2010 to 1.3% in 2013, it failed to cool the market price. Primary and secondary property prices increased by 5.7% and 1%, respectively, 1 year after the introduction of SSD. As SSD effectively increased selling costs and prolonged potential sellers’ holding period, supply from the secondary market dried up and homebuyers were crowded into the primary market. Moreover, the influx of homebuyers from mainland China further crowded out local homebuyers, especially in the primary market, in which mainland buyers’ presence increased from 5.6% in 2009 to 19.7% in 2011. Prior to the SSD, local homebuyers enjoyed a 3% discount over mainland buyers in the primary market. However, this price advantage was diminished due to more intensive competition after the SSD. We also investigate flippers’ responses to SSD policies with respect to their holding period. We find that flippers deferred sales until the stamp duty lock-in period ended, then urgently sold the properties at a discount. Further, those who sold within the lock-in period strategically reduced their selling price. Overall, our paper sheds light on the implications of stamp duty policy on speculative activities and real estate market dynamics.

In a remarkable feat, India has been rather successfully observing an almost 100% lockdown over more than two weeks now, and there is an increasing speculation that the current lockdown may get extended further. While such a lockdown - and extensions of it - are imperative to arrest the spread of virus, but given the heterogeneous nature of Indian economy, the economic costs of a blanket lockdown can pile up sooner than we think, and be more acute than we can imagine. And this begets the question: is 100% lockdown too excessive for India? To address this, I ask the following: can the lockdown policy be fine-tuned to internalize the economic structure of India? The key premise is the following: At the heart of both virus transmission and economic activity is the human-to-human contact. Intuitively, the economic activities differ widely in terms of the frequency with which one has to “transact” with other economic agents. To capture this variance, I define and create a measure of “economic contact-intensity” (ECI Index) using the data available on Indian industries, at the district and industry sector level. The results suggest substantial variation across industrial sectors and the districts, and therefore highlighting an important first-order role for economic contact-intensity to be useful in rationalizing the lockdown policy. Policy implications: I propose a geographical and industry based strategic approach to effectuating the unwinding or the extensions of the lockdown policy. I identify districts and economic sectors, with a combination of lower ECI Index and higher output share which can be preferred to have a lenient lockdown without necessarily compromising on either the virus transmission or the economic output. Also, ceteris paribus, the economic costs for industrial sectors and geographical locations which are on the higher end of contact-intensity are likely to be relatively greater in the event of a lockdown. Therefore, such an identification of the low and high contact-intensity sectors (or districts) could be a useful device for policy makers, if, for instance, fiscal transfers (taxes or subsidies) are instituted in future, to partially mitigate the asymmetric cost implications of a blanket lockdown.

We study the changing relationship between Brent oil prices and geopolitical risk, conditional on physical oil market conditions. We conduct the analysis at three frequencies, medium (1-3 years), high (2-3 months), and very high (daily), using three complementary techniques at the different levels (respectively, continuous wavelet partial coherence, VAR and GARCH-MIDAS) over the period April 1993 to the end of 2018. At the annual frequency, we find evidence of a sustained positive relationship between oil prices and geopolitical risk over the past decade -- with geopolitical risk leading during the Arab Spring, resulting in a substantial geopolitical risk premium, and lagging thereafter by about two months, as oil markets first reacted to and then anticipated geopolitical events. At the monthly frequency, we find the same positive correlation with oil prices anticipating geopolitical risk in both parts of the sample and find that realized geopolitical strife has not led to higher prices in either subsample. At the daily frequency, we find that geopolitical risk has had a positive effect on oil price volatility in later days during the second half of the sample (2005 to 2018). Our findings suggest that some financial market speculators, such as macro hedge funds and algorithmic traders, may amass long positions in Brent in anticipation of geopolitical threats that might potentially lead to oil disruptions.

This paper studies buybacks in a Kyle-type model with many informed parties: a manager implementing buybacks and speculators trading for themselves. Buybacks introduce two opposing forces. They compete against the speculators' trades, making informed trading less profitable. They also increase (decrease) the firm's per-share value when its shares are undervalued (overvalued), making informed trading more profitable. Less informative buybacks weaken the first force while strengthening the second. The manager's incentive to manipulate the current stock price to increase managerial compensation constrains how much information buybacks contain. The model generates novel predictions linking the structure of managerial compensation, buybacks, and trading outcomes.