Quantitative Finance

Chen, Andrew Y.

doi: 10.48550/arxiv.2206.15365pmid: N/A

Abstract:I develop simple and intuitive bounds for the false discovery rate (FDR) in cross-sectional return predictability publications. The bounds can be calculated by plugging in summary statistics from previous papers and reliably bound the FDR in simulations that closely mimic cross-predictor correlations. Most bounds find that at least 75% of findings are true. The tightest bound finds that at least 91% of findings are true. Surprisingly, the estimates in Harvey, Liu, and Zhu (2016) imply a similar FDR. I explain how Harvey et al.'s conclusion that most findings are false stems from misinterpreting ``insignificant factor'' as ``false discovery.''

Reyes, Germán

doi: 10.48550/arxiv.2206.01114pmid: N/A

Abstract:This paper shows that the bunching of wages at round numbers is partly driven by firm coarse wage-setting. Using data from over 200 million new hires in Brazil, I first establish that contracted salaries tend to cluster at round numbers. Then, I show that firms that tend to hire workers at round-numbered salaries have worse market outcomes. Next, I develop a wage-posting model in which optimization costs lead to the adoption of coarse rounded wages and provide evidence supporting two model predictions using two research designs. Finally, I examine some consequences of coarse wage-setting for relevant economic outcomes.

Han, Xia;Lin, Liyuan;Wang, Ruodu

doi: 10.48550/arxiv.2206.13679pmid: N/A

Abstract:We establish the first axiomatic theory for diversification indices using six intuitive axioms: non-negativity, location invariance, scale invariance, rationality, normalization, and continuity. The unique class of indices satisfying these axioms, called the diversification quotients (DQs), are defined based on a parametric family of risk measures. A further axiom of portfolio convexity pins down DQ based on coherent risk measures. DQ has many attractive properties, and it can address several theoretical and practical limitations of existing indices. In particular, for the popular risk measures Value-at-Risk and Expected Shortfall, the corresponding DQ admits simple formulas and it is efficient to optimize in portfolio selection. Moreover, it can properly capture tail heaviness and common shocks, which are neglected by traditional diversification indices. When illustrated with financial data, DQ is intuitive to interpret, and its performance is competitive against other diversification indices.

Zou, Yanzhao;Herremans, Dorien

doi: 10.1016/j.eswa.2023.120838pmid: N/A

Abstract:Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. In an in-depth study, we explore whether social media discussions from the general public on Bitcoin have predictive power for extreme price movements. A dataset of 5,000 tweets per day containing the keyword `Bitcoin' was collected from 2015 to 2021. This dataset, called PreBit, is made available online. In our hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. By combining these embeddings with a Convolutional Neural Network, we built a predictive model for significant market movements. The final multimodal ensemble model includes this NLP model together with a model based on candlestick data, technical indicators and correlated asset prices. In an ablation study, we explore the contribution of the individual modalities. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can used to build a profitable trading strategy with a reduced risk over a `hold' or moving average strategy.

Gankhuu, Battulga

doi: 10.48550/arxiv.2206.09666pmid: N/A

Abstract:For a public company, pricing and hedging models of options and equity--linked life insurance products have been sufficiently developed. However, for a private company, because of unobserved prices, pricing and hedging models of the European options and life insurance products are in their early stages of development. For this reason, this paper introduces a log private company valuation model, which is based on the dynamic Gordon growth model. In this paper, we obtain closed--form pricing and hedging formulas of the European options and equity--linked life insurance products for private companies. Also, the paper provides Maximum Likelihood (ML) estimators of our model, Expectation Maximization (EM) algorithm, and valuation formula for private companies.