nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒01‒30
six papers chosen by

  1. Stock Market Prediction via Deep Learning Techniques: A Survey By Jinan Zou; Qingying Zhao; Yang Jiao; Haiyao Cao; Yanxi Liu; Qingsen Yan; Ehsan Abbasnejad; Lingqiao Liu; Javen Qinfeng Shi
  2. Crypocurrency co-investment network: token returns reflect investment patterns By Luca Mungo; Bartolucci Silvia; Laura Alessandretti
  3. Asset Pricing with Panel Tree Under Global Split Criteria By Lin William Cong; Guanhao Feng; Jingyu He; Xin He
  4. Search Frictions and Product Design in the Municipal Bond Market By Giulia Brancaccio; Karam Kang
  5. Does FinTech Promote Entrepreneurship? Evidence from China By Alraqeb Zeynep; Knaack Peter; Macaire Camille
  6. Visible Hands: Professional Asset Managers' Expectations and the Stock Market in China By John Ammer; John H. Rogers; Gang Wang; Yang Yu

  1. By: Jinan Zou; Qingying Zhao; Yang Jiao; Haiyao Cao; Yanxi Liu; Qingsen Yan; Ehsan Abbasnejad; Lingqiao Liu; Javen Qinfeng Shi
    Abstract: The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction.
    Date: 2022–12
  2. By: Luca Mungo; Bartolucci Silvia; Laura Alessandretti
    Abstract: Since the introduction of Bitcoin in 2009, the dramatic and unsteady evolution of the cryptocurrency market has also been driven by large investments by traditional and cryptocurrency-focused hedge funds. Notwithstanding their critical role, our understanding of the relationship between institutional investments and the evolution of the cryptocurrency market has remained limited, also due to the lack of comprehensive data describing investments over time. In this study, we present a quantitative study of cryptocurrency institutional investments based on a dataset collected for 1324 currencies in the period between 2014 and 2022 from Crunchbase, one of the largest platforms gathering business information. We show that the evolution of the cryptocurrency market capitalization is highly correlated with the size of institutional investments, thus confirming their important role. Further, we find that the market is dominated by the presence of a group of prominent investors who tend to specialise by focusing on particular technologies. Finally, studying the co-investment network of currencies that share common investors, we show that assets with shared investors tend to be characterized by similar market behavior. Our work sheds light on the role played by institutional investors and provides a basis for further research on their influence in the cryptocurrency ecosystem.
    Date: 2023–01
  3. By: Lin William Cong; Guanhao Feng; Jingyu He; Xin He
    Abstract: We develop a new class of tree-based models (P-Tree) for analyzing (unbalanced) panel data utilizing global (instead of local) split criteria that incorporate economic guidance to guard against overfitting while preserving interpretability. We grow a P-Tree top-down to split the cross section of asset returns to construct stochastic discount factor and test assets, generalizing sequential security sorting and visualizing (asymmetric) nonlinear interactions among firm characteristics and macroeconomic states. Data-driven P-Tree models reveal that idiosyncratic volatility and earnings-to-price ratio interact to drive cross-sectional return variations in U.S. equities; market volatility and inflation constitute the most critical regime-switching that asymmetrically interact with characteristics. P-Trees outperform most known observable and latent factor models in pricing individual stocks and test portfolios, while delivering transparent trading strategies and risk-adjusted investment outcomes (e.g., out-of-sample annualized Sharp ratios of about 3 and monthly alpha around 0.8%).
    JEL: C1 G11 G12
    Date: 2022–12
  4. By: Giulia Brancaccio; Karam Kang
    Abstract: This paper shows that product design shapes search frictions and that intermediaries leverage this channel to increase their rents in the context of the U.S. municipal bond market. The majority of bonds are designed via negotiations between a local government and its underwriter. They are then traded in a decentralized market, where the underwriter often also acts as an intermediary. Exploiting variations in state regulations that limit government officials’ conflicts of interest, we provide evidence that bond design from the government’s perspective involves a trade-off between flexibility and liquidity, but the underwriter benefits from designing and trading complex bonds. Motivated by these findings, we build and estimate a model of bond origination and trades to quantify market inefficiency driven by underwriters’ role in intermediating trades and discuss policy implications.
    JEL: L0 L12 L15 P0
    Date: 2022–12
  5. By: Alraqeb Zeynep; Knaack Peter; Macaire Camille
    Abstract: The rise of financial technology (FinTech) in China over the past decade has changed the traditional financial landscape in the country. We provide evidence on the role of digital financial services in promoting self-employment. We construct an indicator of relative FinTech adoption at the provincial-level in China. We show that the digitalization of financial services at an aggregated level is associated with a higher share of self-employed individuals in the total population. In rural areas, coverage breadth of digitalized financial services drives the positive impact on the share of self-employment, while in urban areas, digitalized insurance services appear to be more influential. We also show that the shift to self-employment is not at the expense of employment in private firms in the country. <p> L'essor des technologies financières (FinTech) en Chine au cours de la dernière décennie a modifié le paysage financier traditionnel du pays. Nous étudions la relation entre l’adoption de ces services et la part de l’entreprenariat dans la population totale. Nous construisons un indicateur de l'adoption relative des FinTech au niveau provincial dans le pays, et montrons que la numérisation des services financiers est associée à une part plus élevée d’autoentrepreneurs dans la population totale. Dans les zones rurales, l'étendue de la couverture des services financiers numérisés est à l'origine de cet impact positif, tandis que dans les zones urbaines, les services d'assurance numérisés semblent avoir plus d'influence. Nous montrons également que le passage à l'emploi indépendant ne se fait pas au détriment de l'emploi au sein des entreprises privées dans le pays.
    Keywords: Fintech, Financial Inclusion, Digitalization, China, Entrepreneurship; Fintech, inclusion financière, digitalisation, Chine, entreprenariat
    JEL: G23 J21 O33
    Date: 2022
  6. By: John Ammer; John H. Rogers; Gang Wang; Yang Yu
    Abstract: We study how professional fund managers' growth expectations affect the actions they take with respect to equity investment and in turn the effects on prices. Using novel data on China's mutual fund managers' growth expectations, we show that pessimistic managers decrease equity allocations and shift away from more-cyclical stocks. We identify a strong short-run causal effect of growth expectations on stock returns, despite statistically significant delays in price discovery from short-sale constraints. Finally, we find that an earnings-based measure of price informativeness is increasing in fund investment.
    Keywords: mutual fund managers; chinese financial markets; economic growth expectations; price informativeness; textual analysis
    JEL: D80 E66 G11 G12 G23
    Date: 2022–12

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.