nep-fmk New Economics Papers
on Financial Markets
Issue of 2024‒05‒06
seven papers chosen by

  1. Where Have All the Alphas Gone? A Meta-Analysis of Hedge Fund Performance By Yang, Fan; Havranek, Tomas; Irsova, Zuzana; Novak, Jiri
  2. BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights By Enmin Zhu
  3. “My Name Is Bond. Green Bond.” Informational Efficiency of Climate Finance Markets By Marc Gronwald; Sania Wadud
  4. What Hundreds of Economic News Events Say About Belief Overreaction in the Stock Market By Francesco Bianchi; Sydney C. Ludvigson; Sai Ma
  5. Quantum computing approach to realistic ESG-friendly stock portfolios By Francesco Catalano; Laura Nasello; Daniel Guterding
  6. Using Machine Learning to Forecast Market Direction with Efficient Frontier Coefficients By Nolan Alexander; William Scherer
  7. Market Efficiency Perspective of Precious Metals: Evidence from Developed and Emerging Economies By Rana, Hafiz Muhammad Usman; O'Connor, Fergal; Yerushalmi, Erez; Kim, H. Jae

  1. By: Yang, Fan; Havranek, Tomas; Irsova, Zuzana; Novak, Jiri
    Abstract: We examine the factors influencing published estimates of hedge fund performance. Using a sample of 1, 019 intercept terms from regressions of hedge fund returns on risk factors (the alphas) collected from 74 studies, we document a strong downward trend in the reported alphas. The trend persists even after controlling for heterogeneity in hedge fund characteristics and research design choices in the underlying studies. Estimates of current performance implied by best practice methodology are close to zero across all common hedge fund strategies. Additionally, our data allow us to estimate the mean management and performance fees charged by hedge funds. We also document how reported performance estimates vary with hedge fund and study characteristics. Overall, our findings indicate that, while hedge funds historically generated positive value for investors, their ability to do so has diminished substantially.
    Keywords: Hedge funds, alpha, performance, fees, meta-analysis, model uncertainty
    JEL: J23 J24 J31
    Date: 2024
  2. By: Enmin Zhu
    Abstract: This paper explores the intersection of Natural Language Processing (NLP) and financial analysis, focusing on the impact of sentiment analysis in stock price prediction. We employ BERTopic, an advanced NLP technique, to analyze the sentiment of topics derived from stock market comments. Our methodology integrates this sentiment analysis with various deep learning models, renowned for their effectiveness in time series and stock prediction tasks. Through comprehensive experiments, we demonstrate that incorporating topic sentiment notably enhances the performance of these models. The results indicate that topics in stock market comments provide implicit, valuable insights into stock market volatility and price trends. This study contributes to the field by showcasing the potential of NLP in enriching financial analysis and opens up avenues for further research into real-time sentiment analysis and the exploration of emotional and contextual aspects of market sentiment. The integration of advanced NLP techniques like BERTopic with traditional financial analysis methods marks a step forward in developing more sophisticated tools for understanding and predicting market behaviors.
    Date: 2024–04
  3. By: Marc Gronwald; Sania Wadud
    Abstract: This paper investigates the informational efficiency of green bond markets using a recently introduced quantitative measure for market inefficiency. The methodology assesses the deviation of observed asset price behavior from the Random Walk benchmark, which represents an efficient market. The main findings of the analysis are as follows: the degree of informational inefficiency of the green bond market is generally found to be very similar to that of benchmark bond markets such as treasury bond markets. For extensive periods, what is more, it is even found to be less inefficient. Overall, the price developments in green bond markets are very similar to those in the benchmark bond markets. In other words, fundamental factors that drive bond prices in general also drive prices for green bonds. It is worth pointing out, however, that the degree of inefficiency of the green bond market during the Covid outbreak in 2020 and the inflation shock in 2022/2023 is lower than that of the treasury bond market.
    Keywords: green bonds, efficient market hypothesis, fractional integration
    JEL: C22 E30 G14 Q02 Q31
    Date: 2024
  4. By: Francesco Bianchi; Sydney C. Ludvigson; Sai Ma
    Abstract: We measure the nature and severity of a variety of belief distortions in market reactions to hundreds of economic news events using a new methodology that synthesizes estimation of a structural asset pricing model with algorithmic machine learning to quantify bias. We estimate that investors systematically overreact to perceptions about multiple fundamental shocks in a macro-dynamic system, generating asymmetric compositional effects when several counteracting shocks occur simultaneously in real-world events. We show that belief overreaction to all shocks can lead the market to over- or underreact to events, amplifying or dampening volatility.
    JEL: G1 G12 G4 G41
    Date: 2024–04
  5. By: Francesco Catalano; Laura Nasello; Daniel Guterding
    Abstract: Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in fractional increments, practical implementations often resort to approximations, as fractional stocks are typically not tradeable. While these approximations are effective for large investment budgets, they deteriorate as budgets decrease. To alleviate this issue, a discrete Markowitz portfolio theory (DMPT) with finite budgets and integer stock weights can be formulated, but results in a non-polynomial (NP)-hard problem. Recent progress in quantum processing units (QPUs), including quantum annealers, makes solving DMPT problems feasible. Our study explores portfolio optimization on quantum annealers, establishing a mapping between continuous and discrete Markowitz portfolio theories. We find that correctly normalized discrete portfolios converge to continuous solutions as budgets increase. Our DMPT implementation provides efficient frontier solutions, outperforming traditional rounding methods, even for moderate budgets. Responding to the demand for environmentally and socially responsible investments, we enhance our discrete portfolio optimization with ESG (environmental, social, governance) ratings for EURO STOXX 50 index stocks. We introduce a utility function incorporating ESG ratings to balance risk, return, and ESG-friendliness, and discuss implications for ESG-aware investors.
    Date: 2024–04
  6. By: Nolan Alexander; William Scherer
    Abstract: We propose a novel method to improve estimation of asset returns for portfolio optimization. This approach first performs a monthly directional market forecast using an online decision tree. The decision tree is trained on a novel set of features engineered from portfolio theory: the efficient frontier functional coefficients. Efficient frontiers can be decomposed to their functional form, a square-root second-order polynomial, and the coefficients of this function captures the information of all the constituents that compose the market in the current time period. To make these forecasts actionable, these directional forecasts are integrated to a portfolio optimization framework using expected returns conditional on the market forecast as an estimate for the return vector. This conditional expectation is calculated using the inverse Mills ratio, and the Capital Asset Pricing Model is used to translate the market forecast to individual asset forecasts. This novel method outperforms baseline portfolios, as well as other feature sets including technical indicators and the Fama-French factors. To empirically validate the proposed model, we employ a set of market sector ETFs.
    Date: 2024–03
  7. By: Rana, Hafiz Muhammad Usman; O'Connor, Fergal; Yerushalmi, Erez; Kim, H. Jae
    Abstract: This study examines the weak-form market efficiency of international precious metals markets (Gold, Silver, Platinum, and Palladium) using data from 9 domestic markets in their local currencies - rather than a US Dollar price as in most previous studies. We do this by using the Automatic Portmanteau test, Automatic Variance Ratio test, Autoboot Variance ratio test and Generalized Spectral Shape test to look at their evolving efficiency over time.The findings of this study suggest that market efficiency for four precious metals varies over time across both developed and emerging markets. The variation in market efficiency could be attributable to cyclical developments due to technology and the economic cycle. That they do not tend to efficiency together indicates that these markets are fragmented and not as interconnected as might have been assumed due to a variety of factors such as local regulations, market complexity, and differences in the market structure in each country.
    Keywords: Adaptive markets hypothesis; Martingale difference hypothesis; Market Efficiency; Precious Metals; Gold
    Date: 2024–03–25

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NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.