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on Financial Markets |
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Issue of 2023‒11‒06
seven papers chosen by |
| By: | Raquel M. Gaspar; Xu Jiaming |
| Abstract: | This study provides new insights on the relationship between changes in consumer confidence indices worldwide and the performance of European, United States and Chinese stock markets, during the period from 2007 to 2021. We look both into global and industry returns. For the full-time period, we find stock market returns tend to be positively correlated with changes in consumer confidence indices, with significant two-way Granger causal impacts between the two variables for Europe and the United States. For the Chinese stock market we find less pronounced and only one-way impact { changes in consumer con dence indices can Granger explain Chinese stock returns, but not vice versa. In fact, Chinese stock returns only help explaining changes in East Asian consumer confidence index. These results are robust across industries. For the Covid pandemic sub-period, we find some negative correlations between stock market returns and changes in consumer confidence indices. This is particularly evident in China, but it also happens in Europe and United States, at least for some industries, including Health Care. Overall, the connection between the stock market performance and changes in consumer confidence is lower for USA and European stock markets, but it is higher for the Chinese stock market, in terms of the number of significant outcomes. |
| Keywords: | Consumer confidence index, Stock returns, Granger causality. |
| JEL: | G00 G11 G15 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:ise:remwps:wp02922023&r=fmk |
| By: | Zach Y. Brown; Mark L. Egan; Jihye Jeon; Chuqing Jin; Alex A. Wu |
| Abstract: | Index funds are one of the most common ways investors access financial markets and are perceived to be a transparent and low-cost alternative to active investment management. Despite these purported virtues of index fund investing and the introduction of new products and competitors, many funds remain expensive and fund managers appear to exercise substantial market power. Why do index funds have market power? We develop a novel quantitative dynamic model of demand for and supply of index funds. In the model, investors are subject to inertia, search frictions, and have heterogeneous preferences. These frictions on the demand side create market power for index fund managers, which fund managers can further exploit by price discriminating and charging higher expense ratios to retail investors. Our results suggest that the average expense ratios paid by retail investors are roughly 45% higher as a result of search frictions and are 40% higher as a result of inertia compared to the friction-less baseline. In our counterfactuals, we find an interaction between search frictions and inertia—inertia imposes higher (lower) costs on investors when search frictions are low (high). |
| JEL: | G11 G2 G5 L0 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31778&r=fmk |
| By: | A. H. Nzokem |
| Abstract: | The S&P 500 index is considered the most popular trading instrument in financial markets. With the rise of cryptocurrencies over the past years, Bitcoin has also grown in popularity and adoption. The paper aims to analyze the daily return distribution of the Bitcoin and S&P 500 index and assess their tail probabilities through two financial risk measures. As a methodology, We use Bitcoin and S&P 500 Index daily return data to fit The seven-parameter General Tempered Stable (GTS) distribution using the advanced Fast Fractional Fourier transform (FRFT) scheme developed by combining the Fast Fractional Fourier (FRFT) algorithm and the 12-point rule Composite Newton-Cotes Quadrature. The findings show that peakedness is the main characteristic of the S&P 500 return distribution, whereas heavy-tailedness is the main characteristic of the Bitcoin return distribution. The GTS distribution shows that $80.05\%$ of S&P 500 returns are within $-1.06\%$ and $1.23\%$ against only $40.32\%$ of Bitcoin returns. At a risk level ($\alpha$), the severity of the loss ($AVaR_{\alpha}(X)$) on the left side of the distribution is larger than the severity of the profit ($AVaR_{1-\alpha}(X)$) on the right side of the distribution. Compared to the S&P 500 index, Bitcoin has $39.73\%$ more prevalence to produce high daily returns (more than $1.23\%$ or less than $-1.06\%$). The severity analysis shows that at a risk level ($\alpha$) the average value-at-risk ($AVaR(X)$) of the bitcoin returns at one significant figure is four times larger than that of the S&P 500 index returns at the same risk. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.02436&r=fmk |
| By: | Hsiang-Hui Liu; Han-Jay Shu; Wei-Ning Chiu |
| Abstract: | We introduce NoxTrader, which is designed for portfolio construction and trading execution, aims at generating profitable outcomes. The primary focus of NoxTrader is on stock market trading with an emphasis on cultivating moderate to long-term profits. The underlying learning process of NoxTrader hinges on the assimilation of insights gleaned from historical trading data, primarily hinging on time-series analysis due to the inherent nature of the employed dataset. We delineate the sequential progression encompassing data acquisition, feature engineering, predictive modeling, parameter configuration, establishment of a rigorous backtesting framework, and ultimately position NoxTrader as a testament to the prospective viability of algorithmic trading models within real-world trading scenarios. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.00747&r=fmk |
| By: | Vaibhav Sherkar; Rituparna Sen |
| Abstract: | A property of data which is common across a wide range of instruments, markets and time periods is known as stylized empirical fact in the financial statistics literature. This paper first presents a wide range of stylized facts studied in literature which include some univariate distributional properties, multivariate properties and time series related properties of the financial time series data. In the next part of the paper, price data from several stocks listed on 10 stock exchanges spread across different continents has been analysed and data analysis has been presented. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.00753&r=fmk |
| By: | Jakub Michańków (Cracow University of Economics, Department of Informatics; University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance); Paweł Sakowski (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance) |
| Abstract: | This paper proposes a novel approach to hedging portfolios of risky assets when financial markets are affected by financial turmoils. We introduce a completely novel approach to diversification activity not on the level of single assets but on the level of ensemble algorithmic investment strategies (AIS) built based on the prices of these assets. We employ four types of diverse theoretical models (LSTM - Long Short-Term Memory, ARIMA-GARCH - Autoregressive Integrated Moving Average - Generalized Autoregressive Conditional Heteroskedasticity, momentum, and contrarian) to generate price forecasts, which are then used to produce investment signals in single and complex AIS. In such a way, we are able to verify the diversification potential of different types of investment strategies consisting of various assets (energy commodities, precious metals, cryptocurrencies, or soft commodities) in hedging ensemble AIS built for equity indices (S&P 500 index). Empirical data used in this study cover the period between 2004 and 2022. Our main conclusion is that LSTM-based strategies outperform the other models and that the best diversifier for the AIS built for the S&P 500 index is the AIS built for Bitcoin. Finally, we test the LSTM model for a higher frequency of data (1 hour). We conclude that it outperforms the results obtained using daily data. |
| Keywords: | machine learning, recurrent neural networks, long short-term memory, algorithmic investment strategies, testing architecture, loss function, walk-forward optimization, over-optimization |
| JEL: | C4 C14 C45 C53 C58 G13 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2023-25&r=fmk |
| By: | Zhengyong Jiang; Jeyan Thiayagalingam; Jionglong Su; Jinjun Liang |
| Abstract: | In this paper, we present a novel trading strategy that integrates reinforcement learning methods with clustering techniques for portfolio management in multi-period trading. Specifically, we leverage the clustering method to categorize stocks into various clusters based on their financial indices. Subsequently, we utilize the algorithm Asynchronous Advantage Actor-Critic to determine the trading actions for stocks within each cluster. Finally, we employ the algorithm DDPG to generate the portfolio weight vector, which decides the amount of stocks to buy, sell, or hold according to the trading actions of different clusters. To the best of our knowledge, our approach is the first to combine clustering methods and reinforcement learning methods for portfolio management in the context of multi-period trading. Our proposed strategy is evaluated using a series of back-tests on four datasets, comprising a of 800 stocks, obtained from the Shanghai Stock Exchange and National Association of Securities Deal Automated Quotations sources. Our results demonstrate that our approach outperforms conventional portfolio management techniques, such as the Robust Median Reversion strategy, Passive Aggressive Median Reversion Strategy, and several machine learning methods, across various metrics. In our back-test experiments, our proposed strategy yields an average return of 151% over 360 trading periods with 800 stocks, compared to the highest return of 124% achieved by other techniques over identical trading periods and stocks. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.01319&r=fmk |