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In recent years, machine learning (ML) has brought effective approaches and novel techniques to economic decision, investment forecasting, and risk management, etc., coping the variable and intricate nature of economic and financial environments. For the investment in stock market, this research introduces a pioneering quantitative fusion model combining stock timing and picking strategy by leveraging the Multivariate Gaussian-Hidden Markov Model (MGHMM) and Back Propagation Neural Network optimized by Particle Swarm (PSO-BPNN). After the information coefficients (IC) between fifty-two factors that have been winsorized, neutralized and standardized and the return of CSI 300 index are calculated, a given amount of factors that rank ahead are choose to be candidate factors heading for the input of PSO-BPNN after dimension reduction by Principal Component Analysis (PCA), followed by a certain amount of constituent stocks outputted. Subsequently, we conduct the prediction and trading on the basis of the screening stocks and stock market state outputted by MGHMM trained using inputting CSI 300 index data after Box-Cox transformation, bespeaking eximious performance during the period of past four years. Ultimately, some conventional forecast and trading methods are compared with our strategy in Chinese stock market. Our fusion strategy incorporating stock picking and timing presented in this article provide a innovative technique for financial analysis., Comment: 12 pages, 6 figures, 4 tables, 26 references

Over the past two decades, some scholars have noticed the correlation between quantum mechanics and finance/economy, making some novel attempts to introduce the theoretical framework of quantum mechanics into financial and economic research, subsequently a new research domain called quantum finance or quantum economy was set up. In particular, some studies have made their endeavour in the stock market, utilizing the quantum mechanical paradigm to describe the movement of stock price. Nevertheless, the majority of researches have paid attention to describing the motion of a single stock, and drawn an analogy between the motion of a single stock and a one-dimensional infinite well, or one-dimensional harmonic oscillator model, whose modality looks alike to the one-electron Schr\"odinger equation, in which the information is solved analytically in most cases. Hitherto, the whole stock market system composed of all stocks and stock indexes have not been discussed. In this paper, the concept of stock molecular system is first proposed with pioneer. The modality of stock molecular system resembles the multi-electrons Schr\"odinger equation with Born-Oppenheimer approximation. Similar to the interaction among all nuclei and electrons in a molecule, the interaction exist among all stock indexes and stocks. This paper also establish the stock-index Coulomb potential, stock-index Coulomb potential, stock-stock Coulomb potential and stock coulomb correlation terms by statistical theory. At length, the conceive and feasibility of drawing upon density functional theory (DFT) to solve the Schr\"odinger equation of stock molecular system are put forward together with proof, ending up with experiments executed in CSI 300 index system., Comment: 9 pages, 2 figures, 1 table, 18 references

This paper explores how ambiguity affects communication. We consider a cheap talk model in which the receiver evaluates the sender's message with respect to its worst-case expected payoff generated by multiplier preferences. We characterize the receiver's optimal strategy and show that the receiver's posterior action is consistent with his ex-ante action. We find that in some situations, ambiguity improves communication by shifting the receiver's optimal action upwards, and these situations are not rare.

We study a game between $N$ job applicants who incur a cost $c$ (relative to the job value) to reveal their type during interviews and an administrator who seeks to maximize the probability of hiring the best. We define a full learning equilibrium and prove its existence, uniqueness, and optimality. In equilibrium, the administrator accepts the current best applicant $n$ with probability $c$ if $n

Single component pseudo-potential lattice Boltzmann model has been widely applied in multiphase simulation due to its simplicity and stability. In many research, it has been claimed that this model can be stable for density ratios larger than 1000, however, the application of the model is still limited to small density ratios when the contact angle is considered. The reason is that the original contact angle adjustment method influences the stability of the model. Moreover, simulation results in present work show that, by applying the contact angle adjustment method, the density distribution near the wall is artificially changed, and the contact angle is dependent on the surface tension. Hence, it is very inconvenient to apply this method with a fixed contact angle, and the accuracy of the model cannot be guaranteed. To solve these problems, a contact angle adjustment method based on the geometry analysis is proposed and numerically compared with the original method. Simulation results show that, with the new contact angle adjustment method, the stability of the model is highly improved when the density ratio is relatively large, and it is independent of the surface tension., Comment: 9 figures

Pseudo-potential lattice Boltzmann models have been widely applied in many multiphase simulations. However, most of these models still suffer from some drawbacks such as spurious velocities and untunable surface tension. In this paper, we aim to discuss the surface tension of a popular pseudo-potential model proposed by Kupershtokh et al., which has attracted much attention due to its simplicity and stability. The influence of a parameter on the surface tension in the model is analyzed. Based on the analysis, we proposed a method to adjust surface tension by changing the parameter in the model. However, the density distribution and the stability of the model also depend on the parameter. To adjust the surface tension independently, the pressure tensor modifying method is introduced and numerically tested. The simulation results show that, by applying the pressure tensor modifying method, the surface tension can be adjusted with little influence on the stability and density distributions., Comment: 23 pages, 7 figures, 28 conferences

Single component pseudo-potential lattice Boltzmann models have been widely studied due to their simplicity and stability in multiphase simulations. While numerous model have been proposed, comparative analysis and advantages and disadvantages of different force schemes is often lacking. A pseudo-potential model to simulate large density ratios proposed by Kupershtokh et al. is analyzed in detail in this work. Several common used force schemes are utilized and results compared. Based on the numerical results, the relatively most accurate force scheme proposed by Guo et al. is selected and applied to improve the accuracy of Kupershtokh et al.'s model. Results obtained using the modified Kupershtokh et al.'s model for different value of are compared with those obtained using Li et al.'s model. Effect of relaxation time on the accuracy of the results is reported. Moreover, it is noted that the error in the density ratio predicted by the model is directly correlated with the magnitude of the spurious velocities on (curved) interfaces. Simulation results show that, the accuracy of Kupershtokh et al.'s model can be improved with Guo et al.'s force scheme. However, the errors and relax time's effects are still noticeable when density ratios are large. To improve the accuracy of the pseudo-potential model and to reduce the effects of the relax time, two possible methods were discussed in the present work . Both, a rescaling of the equation of state and multi-relaxation time, are applied and are shown to improve the prediction accuracy of the density ratios., Comment: 31 pages, 6 figures, 7 tables