nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒09‒25
ten papers chosen by
Kwang Soo Cheong, Johns Hopkins University

  1. Do We Price Happiness? Evidence from Korean Stock Market By HyeonJun Kim
  2. The Potential of Quantum Techniques for Stock Price Prediction By Naman S; Gaurang B; Neel S; Aswath Babu H
  3. Investigating Short-Term Dynamics in Green Bond Markets By Lorenzo Mercuri; Andrea Perchiazzo; Edit Rroji
  4. Effects of Daily News Sentiment on Stock Price Forecasting By S. Srinivas; R. Gadela; R. Sabu; A. Das; G. Nath; V. Datla
  5. Do Corporate Bond Shocks Affect Commercial Bank Lending? By Mr. Mario Catalan; Alexander W. Hoffmaister
  6. Green risk in Europe By Nuno Cassola; Claudio Morana; Elisa Ossola
  7. Linking microblogging sentiments to stock price movement: An application of GPT-4 By Rick Steinert; Saskia Altmann
  8. The Financial Market of Environmental Indices By Thisari K. Mahanama; Abootaleb Shirvani; Svetlozar Rachev; Frank J. Fabozzi
  9. Climate Risks and Stock Market Volatility Over a Century in an Emerging Market Economy: The Case of South Africa By Kejin Wu; Sayar Karmakar; Rangan Gupta; Christian Pierdzioch
  10. Differences between NZ and U.S. individual investor sentiment: More noise or more information? By Jędrzej Białkowski; Moritz Wagner; Xiaopeng Wei

  1. By: HyeonJun Kim
    Abstract: This study explores the potential of internet search volume data, specifically Google Trends, as an indicator for cross-sectional stock returns. Unlike previous studies, our research specifically investigates the search volume of the topic 'happiness' and its impact on stock returns in the aspect of risk pricing rather than as sentiment measurement. Empirical results indicate that this 'happiness' search exposure (HSE) can explain future returns, particularly for big and value firms. This suggests that HSE might be a reflection of a firm's ability to produce goods or services that meet societal utility needs. Our findings have significant implications for institutional investors seeking to leverage HSE-based strategies for outperformance. Additionally, our research suggests that, when selected judiciously, some search topics on Google Trends can be related to risks that impact stock prices.
    Date: 2023–08
  2. By: Naman S; Gaurang B; Neel S; Aswath Babu H
    Abstract: We explored the potential applications of various Quantum Algorithms for stock price prediction by conducting a series of experimental simulations using both Classical as well as Quantum Hardware. Firstly, we extracted various stock price indicators, such as Moving Averages (MA), Average True Range (ATR), and Aroon, to gain insights into market trends and stock price movements. Next, we employed Quantum Annealing (QA) for feature selection and Principal Component Analysis (PCA) for dimensionality reduction. Further, we transformed the stock price prediction task essentially into a classification problem. We trained the Quantum Support Vector Machine (QSVM) to predict price movements (whether up or down) contrasted their performance with classical models and analyzed their accuracy on a dataset formulated using Quantum Annealing and PCA individually. We focused on the stock price prediction and binary classification of stock prices for four different companies, namely Apple, Visa, Johnson and Jonson, and Honeywell. We primarily used the real-time stock data of the raw stock prices of these companies. We compared various Quantum Computing techniques with their classical counterparts in terms of accuracy and F-score of the prediction model. Through these experimental simulations, we shed light on the potential advantages and limitations of Quantum Algorithms in stock price prediction and contribute to the growing body of knowledge at the intersection of Quantum Computing and Finance.
    Date: 2023–08
  3. By: Lorenzo Mercuri; Andrea Perchiazzo; Edit Rroji
    Abstract: The paper investigates the effect of the label green in bond markets from the lens of the trading activity. The idea is that jumps in the dynamics of returns have a specific memory nature that can be well represented through a self-exciting process. Specifically, using Hawkes processes where the intensity is described through a continuous time moving average model, we study the high-frequency dynamics of bond prices. We also introduce a bivariate extension of the model that deals with the cross-effect of upward and downward price movements. Empirical results suggest that differences emerge if we consider periods with relevant interest rate announcements, especially in the case of an issuer operating in the energy market.
    Date: 2023–08
  4. By: S. Srinivas; R. Gadela; R. Sabu; A. Das; G. Nath; V. Datla
    Abstract: Predicting future prices of a stock is an arduous task to perform. However, incorporating additional elements can significantly improve our predictions, rather than relying solely on a stock's historical price data to forecast its future price. Studies have demonstrated that investor sentiment, which is impacted by daily news about the company, can have a significant impact on stock price swings. There are numerous sources from which we can get this information, but they are cluttered with a lot of noise, making it difficult to accurately extract the sentiments from them. Hence the focus of our research is to design an efficient system to capture the sentiments from the news about the NITY50 stocks and investigate how much the financial news sentiment of these stocks are affecting their prices over a period of time. This paper presents a robust data collection and preprocessing framework to create a news database for a timeline of around 3.7 years, consisting of almost half a million news articles. We also capture the stock price information for this timeline and create multiple time series data, that include the sentiment scores from various sections of the article, calculated using different sentiment libraries. Based on this, we fit several LSTM models to forecast the stock prices, with and without using the sentiment scores as features and compare their performances.
    Date: 2023–08
  5. By: Mr. Mario Catalan; Alexander W. Hoffmaister
    Abstract: Understanding how corporate bond market disruptions are transmitted to the rest of the financial system is essential to gauge systemic financial risk and design policy responses. In this study, we extend the vector autoregression model of Gilchrist and Zakrajšek (2012) to explicitly account for the role of commercial banks in the transmission of corporate bond credit spread shocks. We find that corporate bond market shocks can reduce commercial bank lending activity by tightening loan supply. Policies designed to contain stress in the corporate bond market can thus mitigate systemic risk by limiting contagion to the commercial banking sector.
    Keywords: excess bond premium; banks; VAR models; financial markets and the macroeconomy; systemic risk; contagion.
    Date: 2023–08–04
  6. By: Nuno Cassola (CEMAPRE, University of Lisbon, Portugal; Center for European Studies, University of Milano-Bicocca, Italy); Claudio Morana (Center for European Studies, University of Milano-Bicocca, Italy; Rimini Centre for Economic Analysis; CeRP, Collegio Carlo Alberto, Italy); Elisa Ossola (Center for European Studies, University of Milano-Bicocca, Italy; Rimini Centre for Economic Analysis)
    Abstract: Climate change poses serious economic, financial, and social challenges to humanity, and green transition policies are now actively implemented in many industrialized countries. Whether financial markets price climate risks is critical to ensuring that the necessary funding flows into environmentally sound projects and that stranded assets risk is adequately managed. In this paper, we assess climate risks for the European stock market within the context of Alessi et al. (2023) greenness and transparency factor. We show that measures of returns spreads of green vs. brown investment might reflect climate risks and assets' exposition to systematic macro-financial risk factors. These latter factors should be filtered out to measure climate risks accurately. We show that climate risks are priced in the European stock market by focusing on aggregate, industry, and company-level data. We propose a market-based green rating procedure, which might be of particular interest to evaluate non-transparent and non-disclosing companies for which ESG information is unavailable. We illustrate its implementation using a sample of over 800 non-transparent firms.
    Keywords: Climate risk, environmental disclosure, macro-finance interface, unconditional factor models, asset pricing, European Union
    JEL: G01 G11 G12 Q54
    Date: 2023–09
  7. By: Rick Steinert; Saskia Altmann
    Abstract: This paper investigates the potential improvement of the GPT-4 Language Learning Model (LLM) in comparison to BERT for modeling same-day daily stock price movements of Apple and Tesla in 2017, based on sentiment analysis of microblogging messages. We recorded daily adjusted closing prices and translated them into up-down movements. Sentiment for each day was extracted from messages on the Stocktwits platform using both LLMs. We develop a novel method to engineer a comprehensive prompt for contextual sentiment analysis which unlocks the true capabilities of modern LLM. This enables us to carefully retrieve sentiments, perceived advantages or disadvantages, and the relevance towards the analyzed company. Logistic regression is used to evaluate whether the extracted message contents reflect stock price movements. As a result, GPT-4 exhibited substantial accuracy, outperforming BERT in five out of six months and substantially exceeding a naive buy-and-hold strategy, reaching a peak accuracy of 71.47 % in May. The study also highlights the importance of prompt engineering in obtaining desired outputs from GPT-4's contextual abilities. However, the costs of deploying GPT-4 and the need for fine-tuning prompts highlight some practical considerations for its use.
    Date: 2023–08
  8. By: Thisari K. Mahanama; Abootaleb Shirvani; Svetlozar Rachev; Frank J. Fabozzi
    Abstract: This paper introduces the concept of a global financial market for environmental indices, addressing sustainability concerns and aiming to attract institutional investors. Risk mitigation measures are implemented to manage inherent risks associated with investments in this new financial market. We monetize the environmental indices using quantitative measures and construct country-specific environmental indices, enabling them to be viewed as dollar-denominated assets. Our primary goal is to encourage the active engagement of institutional investors in portfolio analysis and trading within this emerging financial market. To evaluate and manage investment risks, our approach incorporates financial econometric theory and dynamic asset pricing tools. We provide an econometric analysis that reveals the relationships between environmental and economic indicators in this market. Additionally, we derive financial put options as insurance instruments that can be employed to manage investment risks. Our factor analysis identifies key drivers in the global financial market for environmental indices. To further evaluate the market's performance, we employ pricing options, efficient frontier analysis, and regression analysis. These tools help us assess the efficiency and effectiveness of the market. Overall, our research contributes to the understanding and development of the global financial market for environmental indices.
    Date: 2023–08
  9. By: Kejin Wu (Department of Mathematics, University of California San Diego); Sayar Karmakar (Department of Statistics, University of Florida); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock returns volatility. To this end, we do not only apply the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS model to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock returns volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than classical methods. Our findings have important implications for academics, investors and policymakers.
    Keywords: Climate risks, Volatility forecasting, Model-free prediction, GARCH and GARCHX, South Africa
    JEL: C32 C53 C63 Q54
    Date: 2023–09
  10. By: Jędrzej Białkowski (University of Canterbury); Moritz Wagner (University of Canterbury); Xiaopeng Wei
    Abstract: In this study, we introduce a newly created sentiment index of individual investors in NZ constructed similar to the well-known sentiment index provided by the American Association of Individual Investors (AAII) in the U.S. This unique setup allows us to compare different aspects of investors’ behaviour in both countries. We show that NZ market participants are less confident about the directional movement of the stock market, their expectations are more volatile and their distributions have fatter tails. By contrast, both bullish and bearish sentiment is more persistent among U.S. investors. Furthermore, our analysis of return predictability reveals that both groups of investors behave as noise traders. However, the results for NZ investors are stronger. Overall, our findings call for better financial education, particularly in the area of equity investing.
    Keywords: AAII sentiment index, Information traders, Noise traders, NZ sentiment index, Return predictability
    JEL: G11 G14 G18
    Date: 2023–08–01

This nep-fmk issue is ©2023 by Kwang Soo Cheong. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.