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This article proposes a combination model, which is composed of latent Dirichlet allocation model, TF-IDF feature extraction algorithm and Euclidean distance measurement method, to identify and judge whether the similarities between multiple policy texts exist or not. With the help of actual data result, this will drive the relevant government agencies to figure out problems in a timely manner and provide a decision-making basis for them to formulate and optimise appropriate economic policies. To this end, this article analyses and studies the four types of economic texts that are classified as Insurance, Banking, Tax and Finance from the Central Government of Hebei province and Shijiazhuang city levels. Also, we consider Beijing, Shanghai and Guangdong. Experimental results show that (1) the combination model can quickly and effectively recognise and determine whether there are similarities between multiple economic policy texts; (2) similarities exist or not between the central, provincial and municipal level policy texts depending on the comparison of the distance values across them; (3) the smaller the distance value between economic policy texts of the same kind, the higher the similarity in them; and (4) the distance values between the six policy texts in Finance, Insurance, Bank and Tax categories are ranked from low to high. In terms of similarity, the Finance category is the highest, followed by Insurance and Bank, and the Tax category is the lowest. [ABSTRACT FROM AUTHOR]

We present a new Hawkes-Contact model that combines a Hawkes process and a finite-range contact process to model the stock price movements, especially under the impact of news and other information flows that could lead to contagious effects. To fully capture the underlying price process, we take the Hawkes process to track the full pathway of historical prices on their future movements and the contact process to capture the impact from news/investment sentiment. We compare this full model to a univariate Hawkes process that works as a benchmark model through analyzing their statistical properties using both simulated returns and the real 5-min returns of the crude oil index (Wind CZCE-TA). The statistical properties include probability density function, complementary cumulative distribution function, and Lempel-Ziv Complex. Our results show that the real returns' distribution is often far from normal, but the simulated returns through the Hawkes or Hawke-Contact model can achieve close fit to the real returns and exhibit similar statistical properties. More importantly, the Hawkes-Contact model performs better than the simple Hawkes model in capturing characteristics in the return movements, which indicates that the price evolution is also driven by the news sentiment created after them. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal hedging strategy in the presence of market frictions using the Long Short Term Memory Recurrent Neural Network (LSTM-RNN) method, which is a modification of the method proposed in Buehler et al. (Deep hedging. Quant. Finance, 2019, 19(8), 1271–1291). The market frictions are transaction costs, liquidity constraints, trading limits and cost of funds. The loss function is a spectral risk measure. We first make an empirical analysis of the LSTM-RNN model of real option markets, which are the Asian market (domestic market 50 ETF option, Hong Kong Hang Seng Index Option, Nikkei Index Option), the North American market (S&P 500 Index Option) and the European market (FTSE 100 Index Option). The benchmark models are from Leland (Option pricing and replication with transaction costs. J. Finance., 1985, 40(5), 1283–1301), Boyle and Vorst (Option replication in discrete time with transaction costs. J. Finance, 1992, 47(1), 271–293) and Whalley and Wilmott (A hedging strategy and option valuation model with transaction costs. OCIAM Working Paper, Mathematical Institute, Oxford, 1993). Finally, we compare the results from the LSTM-RNN model with benchmark models involving transaction costs for both simulated market data generated by Geometric Brownian Motion (GBM) and the Heston model and real market data. The results show that the LSTM-RNN model outperforms benchmark models for low or medium volatility (<0.8), OTM moneyness and under a certain risk level (<80%) in the GBM market setting. For the 50ETF option market, the LSTM-RNN model outperforms benchmark models for ATM and under a certain risk level (<15%). For the HSI option market, the LSTM-RNN model outperforms benchmark models when transaction costs are smaller than 1.5%. For the Nikkei and S&P 500 option markets, the LSTM-RNN model always outperforms benchmark models. For the FTSE option market, the LSTM-RNN model outperforms benchmark models when moneyness is not too deeply ITM. [ABSTRACT FROM AUTHOR]