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on Financial Markets |
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Issue of 2022‒09‒26
nine papers chosen by |
| By: | Ricard Durall |
| Abstract: | Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets according to certain goals, risk tolerance, and investment horizon. Unfortunately, there is no simple formula that can find the right allocation for every individual. As a result, investors may use different asset allocations' strategy to try to fulfil their financial objectives. In this work, we conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques. In particular, we focus on traditional approaches based on Modern Portfolio Theory, and on machine-learning approaches based on deep reinforcement learning. We assess the model's performance under different market tendency, i.e., both bullish and bearish markets. For reproducibility, we provide the code implementation code in this repository. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07158&r= |
| By: | James J. Choi |
| Abstract: | I survey the advice given by the fifty most popular personal finance books and compare it to the prescriptions of normative academic economic models. Popular advice frequently departs from normative principles derived from economic theory, which should motivate new hypotheses about why households make the financial choices they do, as well as what financial choices households should make. Popular advice is sometimes driven by fallacies, but it tries to take into account the limited willpower individuals have to stick to a financial plan, and its recommended actions are often easily computable by ordinary individuals. I cover advice on savings rates, the advisability of being a wealthy hand-to-mouth consumer, asset allocation, non-mortgage debt management, simultaneous holding of high-interest debt and low-interest savings, and mortgage choices. |
| JEL: | D14 D15 G11 G4 G5 |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30395&r= |
| By: | Bolin Mao (Kyoto Institute of Economic Research, Kyoto University); Chenhui Chu (Graduate School of Informatics, Kyoto University); Yuta Nakashima (Institute for Datability Science, Osaka University); Hajime Nagahara (Institute for Datability Science, Osaka University) |
| Abstract: | The efficient market hypothesis (EMH) plays a fundamental role in modern financial theory. Previous empirical studies have tested the weak and semi-strong forms of EMH with typical financial data, such as historical stock prices and annual earnings. However, few tests have been extended to include alternative data such as tweets. In this study, we use 1) two stock tweet datasets that have different features and 2) nine natural language processing (NLP)-based deep learning models to test the semi-strong form EMH in the United States stock market. None of our experimental results show that stock tweets with NLP-based models can prominently improve the daily stock price prediction accuracy compared with random guesses. Our experiment provides evidence that the semi-strong form of EMH holds in the United States stock market on a daily basis when considering stock tweet information with the NLP-based models. |
| Keywords: | Efficient Market Hypothesis Test, Daily Stock Price Prediction, Stock Tweet, Natural Language Processing |
| JEL: | C4 C5 G1 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:kyo:wpaper:1082&r= |
| By: | Tobias Wand; Martin He{\ss}ler; Oliver Kamps |
| Abstract: | Understanding and forecasting changing market conditions in complex economic systems like the financial market is of great importance to various stakeholders such as financial institutions and regulatory agencies. Based on the finding that the dynamics of sector correlation matrices of the S&P 500 stock market can be described by a sequence of distinct states via a clustering algorithm, we try to identify the industrial sectors dominating the correlation structure of each state. For this purpose, we use a method from Explainable Artificial Intelligence (XAI) on daily S&P 500 stock market data from 1992 to 2012 to assign relevance scores to every feature of each data point. To compare the significance of the features for the entire data set we develop an aggregation procedure and apply a Bayesian change point analysis to identify the most significant sector correlations. We show that the correlation matrix of each state is dominated only by a few sector correlations. Especially the energy and IT sector are identified as key factors in determining the state of the economy. Additionally we show that a reduced surrogate model, using only the eight sector correlations with the highest XAI-relevance, can replicate 90% of the cluster assignments. In general our findings imply an additional dimension reduction of the dynamics of the financial market. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.14106&r= |
| By: | Yoonsik Hong; Yanghoon Kim; Jeonghun Kim; Yongmin Choi |
| Abstract: | A significant number of equity funds are preferred by index funds nowadays, and market sensitivities are instrumental in managing them. Index funds might replicate the index identically, which is, however, cost-ineffective and impractical. Moreover, to utilize market sensitivities to replicate the index partially, they must be predicted or estimated accurately. Accordingly, first, we examine deep learning models to predict market sensitivities. Also, we present pragmatic applications of data processing methods to aid training and generate target data for the prediction. Then, we propose a partial-index-tracking optimization model controlling the net predicted market sensitivities of the portfolios and index to be the same. These processes' efficacy is corroborated by the Korea Stock Price Index 200. Our experiments show a significant reduction of the prediction errors compared with historical estimations, and competitive tracking errors of replicating the index using fewer than half of the entire constituents. Therefore, we show that applying deep learning to predict market sensitivities is promising and that our portfolio construction methods are practically effective. Additionally, to our knowledge, this is the first study that addresses market sensitivities focused on deep learning. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.00780&r= |
| By: | Taylan Kabbani; Ekrem Duman |
| Abstract: | Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with their environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially Observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm reporting a 2.68 Sharpe Ratio on unseen data set (test data). From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07165&r= |
| By: | Jun Lu; Danny Ding |
| Abstract: | Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction. While generative adversarial network (GAN), conditional GAN (CGAN), and autoencoding CGAN (ACGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN or ACGAN framework stands in putting too much emphasis on generating series and finding the internal trends of the series rather than predicting the future trends. In this paper, we introduce a hybrid approach on conditional GAN based on deep generative models that learns the internal trend of historical data while modeling market uncertainty and future trends. We evaluate the model on several real-world datasets from both the US and Europe markets, and show that the proposed HybridCGAN and HybridACGAN models lead to better portfolio allocation compared to the existing Markowitz, CGAN, and ACGAN approaches. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07159&r= |
| By: | Hans K. Hvide; Tom G. Meling; Magne Mogstad; Ola L. Vestad |
| Abstract: | We study the effects of broadband internet use on the investment decisions of individual investors. A public program in Norway provides plausibly exogenous variation in internet use. Our instrumental variables estimates show that internet use causes a substantial increase in stock market participation, driven primarily by increased fund ownership. Existing investors tilt their portfolios towards funds, thereby obtaining more diversified portfolios and higher Sharpe ratios, and do not increase their trading activity in stocks. Overall, access to high-speed internet seems to spur a “democratization of finance”, with individuals making investment decisions that are more in line with the advice from portfolio theory. |
| JEL: | D04 D14 D15 G00 G11 G40 |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30383&r= |
| By: | Jaydip Sen; Arpit Awad; Aaditya Raj; Gourav Ray; Pusparna Chakraborty; Sanket Das; Subhasmita Mishra |
| Abstract: | The stock market offers a platform where people buy and sell shares of publicly listed companies. Generally, stock prices are quite volatile; hence predicting them is a daunting task. There is still much research going to develop more accuracy in stock price prediction. Portfolio construction refers to the allocation of different sector stocks optimally to achieve a maximum return by taking a minimum risk. A good portfolio can help investors earn maximum profit by taking a minimum risk. Beginning with Dow Jones Theory a lot of advancement has happened in the area of building efficient portfolios. In this project, we have tried to predict the future value of a few stocks from six important sectors of the Indian economy and also built a portfolio. As part of the project, our team has conducted a study of the performance of various Time series, machine learning, and deep learning models in stock price prediction on selected stocks from the chosen six important sectors of the economy. As part of building an efficient portfolio, we have studied multiple portfolio optimization theories beginning with the Modern Portfolio theory. We have built a minimum variance portfolio and optimal risk portfolio for all the six chosen sectors by using the daily stock prices over the past five years as training data and have also conducted back testing to check the performance of the portfolio. We look forward to continuing our study in the area of stock price prediction and asset allocation and consider this project as the first stepping stone. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07166&r= |