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on Financial Markets |
| By: | Maciej Wysocki; Robert \'Slepaczuk |
| Abstract: | This research presents a comprehensive evaluation of systematic index option-writing strategies, focusing on S&P500 index options. We compare the performance of hedging strategies using the Black-Scholes-Merton (BSM) model and the Variance-Gamma (VG) model, emphasizing varying moneyness levels and different sizing methods based on delta and the VIX Index. The study employs 1-minute data of S&P500 index options and index quotes spanning from 2018 to 2023. The analysis benchmarks hedged strategies against buy-and-hold and naked option-writing strategies, with a focus on risk-adjusted performance metrics including transaction costs. Portfolio delta approximations are derived using implied volatility for the BSM model and market-calibrated parameters for the VG model. Key findings reveal that systematic option-writing strategies can potentially yield superior returns compared to buy-and-hold benchmarks. The BSM model generally provided better hedging outcomes than the VG model, although the VG model showed profitability in certain naked strategies as a tool for position sizing. In terms of rehedging frequency, we found that intraday hedging in 130-minute intervals provided both reliable protection against adverse market movements and a satisfactory returns profile. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.13908 |
| By: | Elie Bouri (Adnan Kassar School of Business, Lebanese American University, Lebanon); 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.); Onur Polat (Department of Public Finance, Bilecik Seyh Edebali University, Bilecik, Turkiye) |
| Abstract: | Using monthly data from 1871 to 2024 and logistic models with shrinkage estimators, we compare the contribution of stock and oil-market moments (returns, volatility, skewness, and kurtosis) to the accuracy of out-of-sample forecasts of U.S. recessions at various forecast horizons, while controling for various standard macroeconomic predictors and the total connectedness indexes of the moments. Adding stock-market moments to the potential predictors improves significantly the accuracy of out-of-sample forecasts at the long forecast horizon, whereas oil-market moments and connectedness indexes do not contribute much. The lagged recession dummy, the term spread, and stock returns are found to be the top predictors of recessions. |
| Keywords: | Recessions, Stock-market and oil-market moments, Forecasting, Shrinkage estimators, AUC statistics |
| JEL: | C53 E32 E37 G17 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202435 |
| By: | Han Gui |
| Abstract: | To predict the future movements of stock markets, numerous studies concentrate on daily data and employ various machine learning (ML) models as benchmarks that often vary and lack standardization across different research works. This paper tries to solve the problem from a fresh standpoint by aiming to predict the weekly movements, and introducing a novel benchmark of random traders. This benchmark is independent of any ML model, thus making it more objective and potentially serving as a commonly recognized standard. During training process, apart from the basic features such as technical indicators, scaling laws and directional changes are introduced as additional features, furthermore, the training datasets are also adjusted by assigning varying weights to different samples, the weighting approach allows the models to emphasize specific samples. On back-testing, several trained models show good performance, with the multi-layer perception (MLP) demonstrating stability and robustness across extensive and comprehensive data that include upward, downward and cyclic trends. The unique perspective of this work that focuses on weekly movements, incorporates new features and creates an objective benchmark, contributes to the existing literature on stock market prediction. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.09831 |
| By: | Preetha Saha; Jingrao Lyu; Dhruv Desai; Rishab Chauhan; Jerinsh Jeyapaulraj; Philip Sommer; Dhagash Mehta |
| Abstract: | The trading ecosystem of the Municipal (muni) bond is complex and unique. With nearly 2\% of securities from over a million securities outstanding trading daily, determining the value or relative value of a bond among its peers is challenging. Traditionally, relative value calculation has been done using rule-based or heuristics-driven approaches, which may introduce human biases and often fail to account for complex relationships between the bond characteristics. We propose a data-driven model to develop a supervised similarity framework for the muni bond market based on CatBoost algorithm. This algorithm learns from a large-scale dataset to identify bonds that are similar to each other based on their risk profiles. This allows us to evaluate the price of a muni bond relative to a cohort of bonds with a similar risk profile. We propose and deploy a back-testing methodology to compare various benchmarks and the proposed methods and show that the similarity-based method outperforms both rule-based and heuristic-based methods. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.02273 |
| By: | Carlo Nicolini; Monisha Gopalan; Jacopo Staiano; Bruno Lepri |
| Abstract: | We present the first application of modern Hopfield networks to the problem of portfolio optimization. We performed an extensive study based on combinatorial purged cross-validation over several datasets and compared our results to both traditional and deep-learning-based methods for portfolio selection. Compared to state-of-the-art deep-learning methods such as Long-Short Term Memory networks and Transformers, we find that the proposed approach performs on par or better, while providing faster training times and better stability. Our results show that Modern Hopfield Networks represent a promising approach to portfolio optimization, allowing for an efficient, scalable, and robust solution for asset allocation, risk management, and dynamic rebalancing. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.17645 |
| By: | Andres, Raphaela; Niebel, Thomas; Sack, Robin |
| Abstract: | Until today, the question of how digitalisation and, in particular, individual digital technologies affect productivity is still the subject of controversial debate. Using administrative firm-level data provided by the Dutch and the German statistical offices, we investigate the economic importance of data, in particular, the effect of the application of big data analytics (BDA) on labour productivity (LP) at the firm level. We find that a simple binary measure indicating the mere usage of BDA fails to capture the effect of BDA on LP. In contrast, measures of BDA intensity clearly show a positive and statistically significant relationship between BDA and LP, even after controlling for a firm's general digitalisation level. |
| Keywords: | big data analytics, productivity, administrative firm-level data |
| JEL: | L25 O14 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:300678 |
| By: | Yun Liao |
| Abstract: | This study investigates the relationship between bank efficiency and stock market valuation using an unbalanced panel dataset of 42 listed banks in China from 2006 to 2023. We employ a non-radial and non-oriented slack based super-efficiency Data Envelopment Analysis (Super-SBM-UND-VRS based DEA) model, which treats Non-Performing Loans (NPLs) as an undesired output. Our results show that the relationship between super-efficiency and stock market valuation is stronger than that between Return on Asset (ROA) and stock market performance, as measured by Tobin's Q. Notably, the Super-SBM-UND-VRS model yields novel results compared to other efficiency methods, such as the Stochastic Frontier Analysis (SFA) approach and traditional DEA models. Furthermore, our results suggest that bank evaluations benefit from decreased ownership concentration, whereas interest rate liberalization has the opposite effect. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.14734 |