|
on Financial Markets |
| By: | Caio Almeida; Maria Grith; Ratmir Miftachov; Zijin Wang |
| Abstract: | Based on options and realized returns, we analyze risk premia in the Bitcoin market through the lens of the Pricing Kernel (PK). We identify that: 1) The projected PK into Bitcoin returns is W-shaped and steep in the negative returns region; 2) Negative Bitcoin returns account for 33% of the total Bitcoin index premium (BP) in contrast to 70% of S&P500 equity premium explained by negative returns. Applying a novel clustering algorithm to the collection of estimated Bitcoin risk-neutral densities, we find that risk premia vary over time as a function of two distinct market volatility regimes. In the low-volatility regime, the PK projection is steeper for negative returns. It has a more pronounced W-shape than the unconditional one, implying particularly high BP for both extreme positive and negative returns and a high Variance Risk Premium (VRP). In high-volatility states, the BP attributable to positive and negative returns is more balanced, and the VRP is lower. Overall, Bitcoin investors are more worried about variance and downside risk in low-volatility states. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.15195 |
| By: | Opeyemi Sheu Alamu; Md Kamrul Siam |
| Abstract: | A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, are employed to implement models such as Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Moving Average (ARMA). These models are assessed over three-time horizons: short-term (1 year), medium-term (2.5 years), and long-term (5 years), with performance measured by Mean Squared Error (MSE) and Mean Absolute Error (MAE). The stability of the time series is tested using the Augmented Dickey-Fuller (ADF) test. Results reveal that deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data, resulting in more accurate predictions. However, these models require greater computational resources and offer less interpretability than traditional approaches. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies. Future research could incorporate external factors such as social media sentiment and economic indicators, refine model architectures, and explore real-time applications to enhance prediction accuracy and scalability. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.07220 |
| By: | Andrea L. Eisfeldt; Gregor Schubert |
| Abstract: | We provide evidence that the development and adoption of Generative AI is driving a significant technological shift for firms and for financial research. We review the literature on the impact of ChatGPT on firm value and provide directions for future research investigating the impact of this major technology shock. Finally, we review and describe innovations in research methods linked to improvements in AI tools, along with their applications. We offer a practical introduction to available tools and advice for researchers interested in using these tools. |
| JEL: | G0 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33076 |
| By: | Sabrina T. Howell; Dean Parker; Ting Xu |
| Abstract: | The central tension in securities regulation is between protecting investors and enabling broad capital formation. Focusing on VC fund managers, we study key tools of investor protection in private markets: enforcing relationship-based fundraising and restricting eligible investors. A new policy permitting public advertising is disproportionately used by less well-networked, underrepresented fund managers and is less sensitive to local conditions. Yet it has limited take-up because track record matters at arm’s length while strong networks matter in relationship financing; underrepresented managers more often have neither. Arm’s length fundraising also imposes costs to accessing the “crowd” and verifying investors, inducing negative signaling. |
| JEL: | G21 G23 G32 J15 J16 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33080 |
| By: | Zijie Zhao; Roy E. Welsch |
| Abstract: | Leveraging Deep Reinforcement Learning (DRL) in automated stock trading has shown promising results, yet its application faces significant challenges, including the curse of dimensionality, inertia in trading actions, and insufficient portfolio diversification. Addressing these challenges, we introduce the Hierarchical Reinforced Trader (HRT), a novel trading strategy employing a bi-level Hierarchical Reinforcement Learning framework. The HRT integrates a Proximal Policy Optimization (PPO)-based High-Level Controller (HLC) for strategic stock selection with a Deep Deterministic Policy Gradient (DDPG)-based Low-Level Controller (LLC) tasked with optimizing trade executions to enhance portfolio value. In our empirical analysis, comparing the HRT agent with standalone DRL models and the S&P 500 benchmark during both bullish and bearish market conditions, we achieve a positive and higher Sharpe ratio. This advancement not only underscores the efficacy of incorporating hierarchical structures into DRL strategies but also mitigates the aforementioned challenges, paving the way for designing more profitable and robust trading algorithms in complex markets. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.14927 |
| By: | Matías Moretti; Lorenzo Pandolfi; Mr. German Villegas Bauer; Mr. Sergio L. Schmukler; Tomás Williams |
| Abstract: | We present evidence of inelastic demand for risky sovereign bonds and explore its implications for optimal government debt policies. Using monthly changes in the composition of a major international bond index, we identify flow shocks unrelated to fundamentals that shift the available bond supply. From these shocks, we estimate an inverse demand elasticity of -0.30 and show that it increases with countries’ default risk. We formulate a sovereign debt model with endogenous default and inelastic investors, calibrated to our empirical estimates. By penalizing additional borrowing, an inelastic demand acts as a disciplining device that reduces default risk and bond spreads. |
| Keywords: | inelastic financial markets; institutional investors; international capital markets; sovereign debt |
| Date: | 2024–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/227 |
| By: | Kelvin J. L. Koa; Yunshan Ma; Ritchie Ng; Huanhuan Zheng; Tat-Seng Chua |
| Abstract: | Stock portfolios are often exposed to rare consequential events (e.g., 2007 global financial crisis, 2020 COVID-19 stock market crash), as they do not have enough historical information to learn from. Large Language Models (LLMs) now present a possible tool to tackle this problem, as they can generalize across their large corpus of training data and perform zero-shot reasoning on new events, allowing them to detect possible portfolio crash events without requiring specific training data. However, detecting portfolio crashes is a complex problem that requires more than basic reasoning abilities. Investors need to dynamically process the impact of each new information found in the news articles, analyze the the relational network of impacts across news events and portfolio stocks, as well as understand the temporal context between impacts across time-steps, in order to obtain the overall aggregated effect on the target portfolio. In this work, we propose an algorithmic framework named Temporal Relational Reasoning (TRR). It seeks to emulate the spectrum of human cognitive capabilities used for complex problem-solving, which include brainstorming, memory, attention and reasoning. Through extensive experiments, we show that TRR is able to outperform state-of-the-art solutions on detecting stock portfolio crashes, and demonstrate how each of the proposed components help to contribute to its performance through an ablation study. Additionally, we further explore the possible applications of TRR by extending it to other related complex problems, such as the detection of possible global crisis events in Macroeconomics. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.17266 |
| By: | Lars Hornuf; Matthias Mattusch |
| Abstract: | Fintech startups have set out to revolutionize the financial world. However, little is known about how successful and innovative these firms actually are. This paper investigates firm failure, funding success, and innovation capacity using a hand-collected dataset of 892 German fintechs founded between 2000 and 2021. We find that founders with a business degree and entrepreneurial experience have a better chance of obtaining funding, while founder teams with science, technology, engineering, or mathematics backgrounds file more patents. Early third-party endorsements and foreign partnerships substantially increase firm survival. We also establish the following stylized facts: (1) fintechs focusing on business-to-business models and which position themselves as technical providers prove to be more effective; and (2) fintechs competing in segments traditionally reserved for banks are generally less successful and less innovative. These results have important implications for the early-stage success management of fintech firms and the investment decisions of venture capital funds and government startup programs. |
| Keywords: | Fintech industry, firm funding, firm failure, innovation capacity |
| JEL: | G24 M13 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11301 |