nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒07‒10
twelve papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Explaining AI in Finance: Past, Present, Prospects By Barry Quinn
  2. Machine Learning for Socially Responsible Portfolio Optimisation By Taeisha Nundlall; Terence L Van Zyl
  3. Support for Stock Trend Prediction Using Transformers and Sentiment Analysis By Harsimrat Kaeley; Ye Qiao; Nader Bagherzadeh
  4. On random number generators and practical market efficiency By Ben Moews
  5. Market Making with Deep Reinforcement Learning from Limit Order Books By Hong Guo; Jianwu Lin; Fanlin Huang
  6. The Role of Twitter in Cryptocurrency Pump-and-Dumps By David Ardia; Keven Bluteau
  7. Drivers of Flows-Performance Sensitivity in Mutual Funds By Noam Ben-Ze'ev
  8. Predicting Stock Market Time-Series Data using CNN-LSTM Neural Network Model By Aadhitya A; Rajapriya R; Vineetha R S; Anurag M Bagde
  9. Stock and market index prediction using Informer network By Yuze Lu; Hailong Zhang; Qiwen Guo
  10. The Overview and Risks of Fund Finance By KANAGUCHI Takehisa; KAWAKAMI Takehito; HASEBE Akira; OGAWA Yoshiya
  11. Forecasting the Performance of US Stock Market Indices During COVID-19: RF vs LSTM By Reza Nematirad; Amin Ahmadisharaf; Ali Lashgari
  12. A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market By Jaydip Sen; Aditya Jaiswal; Anshuman Pathak; Atish Kumar Majee; Kushagra Kumar; Manas Kumar Sarkar; Soubhik Maji

  1. By: Barry Quinn
    Abstract: This paper explores the journey of AI in finance, with a particular focus on the crucial role and potential of Explainable AI (XAI). We trace AI's evolution from early statistical methods to sophisticated machine learning, highlighting XAI's role in popular financial applications. The paper underscores the superior interpretability of methods like Shapley values compared to traditional linear regression in complex financial scenarios. It emphasizes the necessity of further XAI research, given forthcoming EU regulations. The paper demonstrates, through simulations, that XAI enhances trust in AI systems, fostering more responsible decision-making within finance.
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.02773&r=fmk
  2. By: Taeisha Nundlall; Terence L Van Zyl
    Abstract: Socially responsible investors build investment portfolios intending to incite social and environmental advancement alongside a financial return. Although Mean-Variance (MV) models successfully generate the highest possible return based on an investor's risk tolerance, MV models do not make provisions for additional constraints relevant to socially responsible (SR) investors. In response to this problem, the MV model must consider Environmental, Social, and Governance (ESG) scores in optimisation. Based on the prominent MV model, this study implements portfolio optimisation for socially responsible investors. The amended MV model allows SR investors to enter markets with competitive SR portfolios despite facing a trade-off between their investment Sharpe Ratio and the average ESG score of the portfolio.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.12364&r=fmk
  3. By: Harsimrat Kaeley; Ye Qiao; Nader Bagherzadeh
    Abstract: Stock trend analysis has been an influential time-series prediction topic due to its lucrative and inherently chaotic nature. Many models looking to accurately predict the trend of stocks have been based on Recurrent Neural Networks (RNNs). However, due to the limitations of RNNs, such as gradient vanish and long-term dependencies being lost as sequence length increases, in this paper we develop a Transformer based model that uses technical stock data and sentiment analysis to conduct accurate stock trend prediction over long time windows. This paper also introduces a novel dataset containing daily technical stock data and top news headline data spanning almost three years. Stock prediction based solely on technical data can suffer from lag caused by the inability of stock indicators to effectively factor in breaking market news. The use of sentiment analysis on top headlines can help account for unforeseen shifts in market conditions caused by news coverage. We measure the performance of our model against RNNs over sequence lengths spanning 5 business days to 30 business days to mimic different length trading strategies. This reveals an improvement in directional accuracy over RNNs as sequence length is increased, with the largest improvement being close to 18.63% at 30 business days.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.14368&r=fmk
  4. By: Ben Moews
    Abstract: Modern mainstream financial theory is underpinned by the efficient market hypothesis, which posits the rapid incorporation of relevant information into asset pricing. Limited prior studies in the operational research literature have investigated the use of tests designed for random number generators to check for these informational efficiencies. Treating binary daily returns as a hardware random number generator analogue, tests of overlapping permutations have indicated that these time series feature idiosyncratic recurrent patterns. Contrary to prior studies, we split our analysis into two streams at the annual and company level, and investigate longer-term efficiency over a larger time frame for Nasdaq-listed public companies to diminish the effects of trading noise and allow the market to realistically digest new information. Our results demonstrate that information efficiency varies across different years and reflects large-scale market impacts such as financial crises. We also show the proximity to results of a logistic map comparison, discuss the distinction between theoretical and practical market efficiency, and find that the statistical qualification of stock-separated returns in support of the efficient market hypothesis is dependent on the driving factor of small inefficient subsets that skew market assessments.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.17419&r=fmk
  5. By: Hong Guo; Jianwu Lin; Fanlin Huang
    Abstract: Market making (MM) is an important research topic in quantitative finance, the agent needs to continuously optimize ask and bid quotes to provide liquidity and make profits. The limit order book (LOB) contains information on all active limit orders, which is an essential basis for decision-making. The modeling of evolving, high-dimensional and low signal-to-noise ratio LOB data is a critical challenge. Traditional MM strategy relied on strong assumptions such as price process, order arrival process, etc. Previous reinforcement learning (RL) works handcrafted market features, which is insufficient to represent the market. This paper proposes a RL agent for market making with LOB data. We leverage a neural network with convolutional filters and attention mechanism (Attn-LOB) for feature extraction from LOB. We design a new continuous action space and a hybrid reward function for the MM task. Finally, we conduct comprehensive experiments on latency and interpretability, showing that our agent has good applicability.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.15821&r=fmk
  6. By: David Ardia; Keven Bluteau
    Abstract: We examine the influence of Twitter promotion on cryptocurrency pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. These findings shed light on the pivotal role of Twitter promotion in cryptocurrency manipulation, offering valuable insights into participant behavior and market dynamics.
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.02148&r=fmk
  7. By: Noam Ben-Ze'ev (Bank of Israel)
    Abstract: This paper examines the relationship between mutual fund performance and fund flows in Israel. Israel has a unique setting: Bonds are traded on a limit order book exchange, resulting in high liquidity. Using proprietary daily fund level data, I find a convex performance-flows relationship, meaning investors are more sensitive to good performance than to bad performance, in all three market segments of actively managed funds: government bonds, corporate bonds, and equity. This indicates that the first mover's advantage documented in US corporate bond mutual funds as a source of market fragility, which drives a concave performance-flows relationship, does not exist in Israel, and perhaps more generally in exchanges with a limit order book. I find that flows to passive funds are at minimum 40% less sensitive to performance in comparison to active funds, indicating that passive investments might have a moderating effect at times of financial stress, as flows to them are less procyclical than to active funds.
    Keywords: Financial fragility, liquidity, bond funds, mutual fund flows, passive investment, index tracking funds
    JEL: G01 G18 G20 G23
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:boi:wpaper:2023.06&r=fmk
  8. By: Aadhitya A; Rajapriya R; Vineetha R S; Anurag M Bagde
    Abstract: Stock market is often important as it represents the ownership claims on businesses. Without sufficient stocks, a company cannot perform well in finance. Predicting a stock market performance of a company is nearly hard because every time the prices of a company stock keeps changing and not constant. So, its complex to determine the stock data. But if the previous performance of a company in stock market is known, then we can track the data and provide predictions to stockholders in order to wisely take decisions on handling the stocks to a company. To handle this, many machine learning models have been invented but they didn't succeed due to many reasons like absence of advanced libraries, inaccuracy of model when made to train with real time data and much more. So, to track the patterns and the features of data, a CNN-LSTM Neural Network can be made. Recently, CNN is now used in Natural Language Processing (NLP) based applications, so by identifying the features from stock data and converting them into tensors, we can obtain the features and then send it to LSTM neural network to find the patterns and thereby predicting the stock market for given period of time. The accuracy of the CNN-LSTM NN model is found to be high even when allowed to train on real-time stock market data. This paper describes about the features of the custom CNN-LSTM model, experiments we made with the model (like training with stock market datasets, performance comparison with other models) and the end product we obtained at final stage.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.14378&r=fmk
  9. By: Yuze Lu; Hailong Zhang; Qiwen Guo
    Abstract: Applications of deep learning in financial market prediction has attracted huge attention from investors and researchers. In particular, intra-day prediction at the minute scale, the dramatically fluctuating volume and stock prices within short time periods have posed a great challenge for the convergence of networks result. Informer is a more novel network, improved on Transformer with smaller computational complexity, longer prediction length and global time stamp features. We have designed three experiments to compare Informer with the commonly used networks LSTM, Transformer and BERT on 1-minute and 5-minute frequencies for four different stocks/ market indices. The prediction results are measured by three evaluation criteria: MAE, RMSE and MAPE. Informer has obtained best performance among all the networks on every dataset. Network without the global time stamp mechanism has significantly lower prediction effect compared to the complete Informer; it is evident that this mechanism grants the time series to the characteristics and substantially improves the prediction accuracy of the networks. Finally, transfer learning capability experiment is conducted, Informer also achieves a good performance. Informer has good robustness and improved performance in market prediction, which can be exactly adapted to real trading.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.14382&r=fmk
  10. By: KANAGUCHI Takehisa (Bank of Japan); KAWAKAMI Takehito (Bank of Japan); HASEBE Akira (Bank of Japan); OGAWA Yoshiya (Bank of Japan)
    Abstract: The funds see an increase in their financing demand, depending on their own investment stage, as the inflow into private equity funds and other funds continues. Under these circumstances, financial institutions promote their businesses based on the high profitability of Fund Finance. In addition, they have established a risk management system that pays attention to the risk characteristics associated with such finance. On the other hand, the funds lengthen loan terms and increase the leverage of Fund Finance in order to boost investment returns to investors, and increasing risks associated with Fund Finance have been identified. Therefore, it is important to understand the real picture of Fund Finance and carefully monitor its potential risks.
    Date: 2023–06–19
    URL: http://d.repec.org/n?u=RePEc:boj:bojrev:rev23e05&r=fmk
  11. By: Reza Nematirad; Amin Ahmadisharaf; Ali Lashgari
    Abstract: The US stock market experienced instability following the recession (2007-2009). COVID-19 poses a significant challenge to US stock traders and investors. Traders and investors should keep up with the stock market. This is to mitigate risks and improve profits by using forecasting models that account for the effects of the pandemic. With consideration of the COVID-19 pandemic after the recession, two machine learning models, including Random Forest and LSTM are used to forecast two major US stock market indices. Data on historical prices after the big recession is used for developing machine learning models and forecasting index returns. To evaluate the model performance during training, cross-validation is used. Additionally, hyperparameter optimizing, regularization, such as dropouts and weight decays, and preprocessing improve the performances of Machine Learning techniques. Using high-accuracy machine learning techniques, traders and investors can forecast stock market behavior, stay ahead of their competition, and improve profitability. Keywords: COVID-19, LSTM, S&P500, Random Forest, Russell 2000, Forecasting, Machine Learning, Time Series JEL Code: C6, C8, G4.
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.03620&r=fmk
  12. By: Jaydip Sen; Aditya Jaiswal; Anshuman Pathak; Atish Kumar Majee; Kushagra Kumar; Manas Kumar Sarkar; Soubhik Maji
    Abstract: This paper presents a comparative analysis of the performances of three portfolio optimization approaches. Three approaches of portfolio optimization that are considered in this work are the mean-variance portfolio (MVP), hierarchical risk parity (HRP) portfolio, and reinforcement learning-based portfolio. The portfolios are trained and tested over several stock data and their performances are compared on their annual returns, annual risks, and Sharpe ratios. In the reinforcement learning-based portfolio design approach, the deep Q learning technique has been utilized. Due to the large number of possible states, the construction of the Q-table is done using a deep neural network. The historical prices of the 50 premier stocks from the Indian stock market, known as the NIFTY50 stocks, and several stocks from 10 important sectors of the Indian stock market are used to create the environment for training the agent.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.17523&r=fmk

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