nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒04‒17
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. The Contribution of Transaction Costs to Expected Stock Returns: A Novel Measure By Kazuhiro Hiraki; George Skiadopoulos
  2. NFT Bubbles By Andrea Barbon; Angelo Ranaldo
  3. The Value of Ratings: Evidence from their Introduction in Securities Markets By Asaf Bernstein; Carola Frydman; Eric Hilt
  4. Portfolio Volatility Estimation Relative to Stock Market Cross-Sectional Intrinsic Entropy By Claudiu Vinte; Marcel Ausloos
  5. Venture Capital Portfolio Construction and the Main Factors Impacting the Optimal Strategy By Francesco Farina; Mike Arpaia; Harpal Khing; Jonas Vetterle
  6. Stock Trend Prediction: A Semantic Segmentation Approach By Shima Nabiee; Nader Bagherzadeh
  7. Network log-ARCH models for forecasting stock market volatility By Raffaele Mattera; Philipp Otto
  8. A Myersonian Framework for Optimal Liquidity Provision in Automated Market Makers By Jason Milionis; Ciamac C. Moallemi; Tim Roughgarden
  9. Improving CNN-base Stock Trading By Considering Data Heterogeneity and Burst By Keer Yang; Guanqun Zhang; Chuan Bi; Qiang Guan; Hailu Xu; Shuai Xu
  10. Application of supervised learning models in the Chinese futures market By Fuquan Tang

  1. By: Kazuhiro Hiraki (Institute for Monetary and Economic Studies, Bank of Japan,); George Skiadopoulos (School of Economics and Finance, Queen Mary University of London and Department of Banking and Financial Management, University of Piraeus,)
    Abstract: We document that a theoretically founded, real-time, and easy-to-implement option-based measure, termed synthetic-stock difference (SSD), accurately estimates the part of stock’s expected return arising from stock’s transaction costs. We calculate SSD for U.S. optionable stocks. SSD can be more than 10% per annum, it can fluctuate significantly over time and its cross-sectional dispersion widens over market crises periods. We confirm the accuracy of SSD by empirically verifying the predictions of a general asset pricing setting with transaction costs. First, we document its predicted type of connection with various proxies of stocks’ transaction costs. Second, we conduct simple asset pricing tests which render further support. Our setting allows explaining the size of alphas reported by previous literature on the predictive ability of deviations from put-call parity.
    Keywords: Transaction costs, Put-call parity, Return predictability, Informational content of options
    JEL: C13 G10 G12 G13
    Date: 2023–02–10
  2. By: Andrea Barbon; Angelo Ranaldo
    Abstract: By investigating nonfungible tokens (NFTs), we provide the first systematic study of retail investor behavior through asset bubbles. Given that NFTs are recorded in public blockchains, we are able to track investor behavior over time, leading to the identification of numerous price run-ups and crashes. Our study reveals that agent-level variables, such as investor sophistication, heterogeneity, and wash trading, in addition to aggregate variables, such as volatility, price acceleration, and turnover, significantly predict bubble formation and price crashes. We find that sophisticated investors consistently outperform others and exhibit characteristics consistent with superior information and skills, supporting the narrative surrounding asset pricing bubbles.
    Date: 2023–03
  3. By: Asaf Bernstein; Carola Frydman; Eric Hilt
    Abstract: We study the effects of the first-ever ratings for corporate securities. In 1909, John Moody published a book that partitioned the majority of listed railroad bonds into letter-graded ratings based on his assessments of their credit risk. These ratings had no regulatory implications and were largely explainable using publicly available information. Despite this, we find that lower than market-implied ratings caused a rise in secondary market bond yields. Using an instrumental-variables design, we show that bonds that were rated experienced a substantial decline in their bid-ask spreads, which is consistent with reduced information asymmetries and improved liquidity. Our findings suggest that ratings can improve information transmission, even in settings with the highest monetary stakes, and highlight their potential value for the functioning of financial markets.
    JEL: G24 G28 N21 N81
    Date: 2023–03
  4. By: Claudiu Vinte; Marcel Ausloos
    Abstract: Selecting stock portfolios and assessing their relative volatility risk compared to the market as a whole, market indices, or other portfolios is of great importance to professional fund managers and individual investors alike. Our research uses the cross-sectional intrinsic entropy (CSIE) model to estimate the cross-sectional volatility of the stock groups that can be considered together as portfolio constituents. In our study, we benchmark portfolio volatility risks against the volatility of the entire market provided by the CSIE and the volatility of market indices computed using longitudinal data. This article introduces CSIE-based betas to characterise the relative volatility risk of the portfolio against market indices and the market as a whole. We empirically prove that, through CSIE-based betas, multiple sets of symbols that outperform the market indices in terms of rate of return while maintaining the same level of risk or even lower than the one exhibited by the market index can be discovered, for any given time interval. These sets of symbols can be used as constituent stock portfolios and, in connection with the perspective provided by the CSIE volatility estimates, to hierarchically assess their relative volatility risk within the broader context of the overall volatility of the stock market.
    Date: 2023–03
  5. By: Francesco Farina; Mike Arpaia; Harpal Khing; Jonas Vetterle
    Abstract: The optimal portfolio size for a venture capital (VC) fund is a topic often debated, but there is no consensus on the best strategy. This is because it is a function of many factors. It is not easy to find a general formula that can be applied to all situations, and it largely depends on the goal of the fund. In this report, we will go through the different factors step by step, studying how they affect fund returns and the optimal portfolio size, starting with some basic assumptions and then increasing the complexity of the model.
    Date: 2023–03
  6. By: Shima Nabiee; Nader Bagherzadeh
    Abstract: Market financial forecasting is a trending area in deep learning. Deep learning models are capable of tackling the classic challenges in stock market data, such as its extremely complicated dynamics as well as long-term temporal correlation. To capture the temporal relationship among these time series, recurrent neural networks are employed. However, it is difficult for recurrent models to learn to keep track of long-term information. Convolutional Neural Networks have been utilized to better capture the dynamics and extract features for both short- and long-term forecasting. However, semantic segmentation and its well-designed fully convolutional networks have never been studied for time-series dense classification. We present a novel approach to predict long-term daily stock price change trends with fully 2D-convolutional encoder-decoders. We generate input frames with daily prices for a time-frame of T days. The aim is to predict future trends by pixel-wise classification of the current price frame. We propose a hierarchical CNN structure to encode multiple price frames to multiscale latent representation in parallel using Atrous Spatial Pyramid Pooling blocks and take that temporal coarse feature stacks into account in the decoding stages. Our hierarchical structure of CNNs makes it capable of capturing both long and short-term temporal relationships effectively. The effect of increasing the input time horizon via incrementing parallel encoders has been studied with interesting and substantial changes in the output segmentation masks. We achieve overall accuracy and AUC of %78.18 and 0.88 for joint trend prediction over the next 20 days, surpassing other semantic segmentation approaches. We compared our proposed model with several deep models specifically designed for technical analysis and found that for different output horizons, our proposed models outperformed other models.
    Date: 2023–03
  7. By: Raffaele Mattera; Philipp Otto
    Abstract: This paper presents a novel dynamic network autoregressive conditional heteroscedasticity (ARCH) model based on spatiotemporal ARCH models to forecast volatility in the US stock market. To improve the forecasting accuracy, the model integrates temporally lagged volatility information and information from adjacent nodes, which may instantaneously spill across the entire network. The model is also suitable for high-dimensional cases where multivariate ARCH models are typically no longer applicable. We adopt the theoretical foundations from spatiotemporal statistics and transfer the dynamic ARCH model for processes to networks. This new approach is compared with independent univariate log-ARCH models. We could quantify the improvements due to the instantaneous network ARCH effects, which are studied for the first time in this paper. The edges are determined based on various distance and correlation measures between the time series. The performances of the alternative networks' definitions are compared in terms of out-of-sample accuracy. Furthermore, we consider ensemble forecasts based on different network definitions.
    Date: 2023–03
  8. By: Jason Milionis; Ciamac C. Moallemi; Tim Roughgarden
    Abstract: In decentralized finance ("DeFi"), automated market makers (AMMs) enable traders to programmatically exchange one asset for another. Such trades are enabled by the assets deposited by liquidity providers (LPs). The goal of this paper is to characterize and interpret the optimal (i.e., profit-maximizing) strategy of a monopolist liquidity provider, as a function of that LP's beliefs about asset prices and trader behavior. We introduce a general framework for reasoning about AMMs. In this model, the market maker (i.e., LP) chooses a demand curve that specifies the quantity of a risky asset (such as BTC or ETH) to be held at each dollar price. Traders arrive sequentially and submit a price bid that can be interpreted as their estimate of the risky asset price; the AMM responds to this submitted bid with an allocation of the risky asset to the trader, a payment that the trader must pay, and a revised internal estimate for the true asset price. We define an incentive-compatible (IC) AMM as one in which a trader's optimal strategy is to submit its true estimate of the asset price, and characterize the IC AMMs as those with downward-sloping demand curves and payments defined by a formula familiar from Myerson's optimal auction theory. We characterize the profit-maximizing IC AMM via a generalization of Myerson's virtual values. The optimal demand curve generally has a jump that can be interpreted as a "bid-ask spread, " which we show is caused by a combination of adverse selection risk (dominant when the degree of information asymmetry is large) and monopoly pricing (dominant when asymmetry is small).
    Date: 2023–02
  9. By: Keer Yang; Guanqun Zhang; Chuan Bi; Qiang Guan; Hailu Xu; Shuai Xu
    Abstract: In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i.e., constructing automatic and intelligent trading framework based on historical stock price. Due to the unpredictable, uncertainty and volatile nature of financial market, researchers have also resorted to deep learning to construct the intelligent trading framework. In this paper, we propose to use CNN as the core functionality of such framework, because it is able to learn the spatial dependency (i.e., between rows and columns) of the input data. However, different with existing deep learning-based trading frameworks, we develop novel normalization process to prepare the stock data. In particular, we first empirically observe that the stock data is intrinsically heterogeneous and bursty, and then validate the heterogeneity and burst nature of stock data from a statistical perspective. Next, we design the data normalization method in a way such that the data heterogeneity is preserved and bursty events are suppressed. We verify out developed CNN-based trading framework plus our new normalization method on 29 stocks. Experiment results show that our approach can outperform other comparing approaches.
    Date: 2023–03
  10. By: Fuquan Tang
    Abstract: Based on the characteristics of the Chinese futures market, this paper builds a supervised learning model to predict the trend of futures prices and then designs a trading strategy based on the prediction results. The Precision, Recall and F1-score of the classification problem show that our model can meet the accuracy requirements for the classification of futures price movements in terms of test data. The backtest results show that our trading system has an upward trending return curve with low capital retracement.
    Date: 2023–03

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