A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Model monthly VIX and stock returns using log-Heston model.
Study finds mixed evidence of monthly stock market anomalies in Turkey and US.
Cumulant expansion is used to derive accurate closed-form approximation for Monthly Sum Options in case of constant volatility model. Payoff of Monthly Sum Option is based on sum of caped (and probably floored) returns. It is noticed, that can be used as a small parameter in Edgeworth expansion. First …
This study examines how ChiNext IPOs' initial returns are influenced by regulation regime changes.
This paper clarifies Bitcoin's volatility and predictability across daily, weekly, and monthly scales.
We build a simple diagnostic criterion for approximate factor structure in large cross-sectional equity datasets. Given a model for asset returns with observable factors, the criterion checks whether the error terms are weakly cross-sectionally correlated or share at least one unobservable common factor. It only requir…
Method for factor analysis in short panels without assuming sphericity or Gaussianity.
We empirically test predictability on asset price by using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directio…
We discuss the finding that cross-sectional characteristic based models have yielded portfolios with higher excess monthly returns but lower risk than their arbitrage pricing theory counterparts in an analysis of equity returns of stocks listed on the JSE. Under the assumption of general no-arbitrage conditions, we arg…
This paper examines volatility in REITs using a multivariate GARCH based model. The Multivariate VAR-GARCH technique documents the return and volatility linkages between REIT sub-sectors and also examines the influence of other US equity series. The motivation is for investors to incorporate time-varyng volatility and …
A linear link between S&P 500 return and the change rate of the number of nine-year-olds in the USA has been found. The return is represented by a sum of monthly returns during previous twelve months. The change rate of the specific age population is represented by moving averages. The period between January 1990 and D…
New method improves portfolio allocation using local Gaussian correlation.
A new method models financial returns by separating sign and magnitude, improving forecasting accuracy.
Realized moments of higher order computed from intraday returns are introduced in recent years. The literature indicates that realized skewness is an important factor in explaining future asset returns. However, the literature mainly focuses on the whole market and on the monthly or weekly scale. In this paper, we cond…
Study finds monetary policy uncertainty negatively impacts Bitcoin returns.
Customer momentum is a positive relationship between a firm's returns and past returns of its customers.
Estimates mean and covariance for large, unbalanced stock returns panels.
Machine learning helps estimate risk premiums of stocks without knowing their factors.
Study finds physical momentum portfolios in Indian stock market yield higher returns than benchmarks.
StockGPT predicts stock returns using AI, outperforming traditional strategies.
Study news networks to predict stock returns.
Maximizes stock portfolio predictability using machine learning.
Time-varying neural network improves stock return prediction.
Study uses VIX for zero-coupon Treasury rates, proving long-term stability and returns.
Dynamics of the major USA market indices DJIA, S&P, Nasdaq, and NYSE is analyzed from the point of view of the random walking problem with two-step correlations of the market moves. The parameters characterizing the stochastic dynamics are determined empirically from the historical quotes for the daily, weekly, and mon…
The paper presents new machine learning methods: signal composition, which classifies time-series regardless of length, type, and quantity; and self-labeling, a supervised-learning enhancement. The paper describes further the implementation of the methods on a financial search engine system to identify behavioral simil…
Using a time-varying approach, this paper examines the dynamics of volatility in the REIT sector. The results highlight the attractiveness and suitability of using GARCH based approaches in the modeling of daily REIT volatility. The paper examines the influencing factors on REIT volatility, documenting the return and v…
Unified framework for fast large-scale portfolio optimization.
This study compares Markowitz and Single-Index models for Malaysian stocks.
Study finds monthly SIPs outperform first-day SIPs in Nifty 50 by 0.5-2.5% annually.
FinBERT model identifies key speakers in earnings calls, boosting stock returns.
The p-index improves investment performance for NYSE stocks but not for SSE stocks.
Researchers have constantly asked whether stock returns can be predicted by some macroeconomic data. However, it is known that macroeconomic data may exhibit nonstationarity and/or heavy tails, which complicates existing testing procedures for predictability. In this paper we propose novel empirical likelihood methods …
The paper analyzes debt recycling strategies for mortgage repayment, revealing complex phases of success and failure.
We investigate the volatility return intervals in the NYSE and FOREX markets. We explain previous empirical findings using a model based on the interacting agent hypothesis instead of the widely-used efficient market hypothesis. We derive macroscopic equations based on the microscopic herding interactions of agents and…
Investing in cryptocurrencies can improve portfolio risk-return profile, especially with diversification strategies.
Investigates chaotic financial time series with monthly contributions and devaluation.
We have studied statistical characteristics of five share price time series. For each stock price, we estimated a best fit quantitative model for the monthly closing price as based on the decomposition into two defining consumer price indices selected from a large set of CPIs. It was found that there are two pairs of s…
The proprietary nature of Hedge Fund investing means that it is common practise for managers to release minimal information about their returns. The construction of a Fund of Hedge Funds portfolio requires a correlation matrix which often has to be estimated using a relatively small sample of monthly returns data which…
Optimizes high-dimensional portfolios using joint shrinkage.
New statistical factors improve portfolio risk estimation.
The paper introduces a machine learning method to forecast market direction using efficient frontier coefficients.
Hedge funds have long been viewed as a veritable "black box" of investing since outsiders may never view the exact composition of portfolio holdings. Therefore, the ability to estimate an informative set of asset weights is highly desirable for analysis. We present a compositional state space model for estimation of an…
We investigate the pricing of cliquet options in a geometric Meixner model. The considered option is of monthly sum cap style while the underlying stock price model is driven by a pure-jump Meixner--Lévy process yielding Meixner distributed log-returns. In this setting, we infer semi-analytic expressions for the clique…
We derive a general multivariate theory for realised characteristics of `model-free discretisation-invariant swaps', so-called because the standard no-arbitrage assumption of martingale forward prices is sufficient to derive fair-value swap rates for such characteristics which have no jump or discretisation errors. Thi…
MACE optimizes stock portfolios for maximal predictability.
Sharpe ratio is widely used in asset management to compare and benchmark funds and asset managers. It computes the ratio of the excess return over the strategy standard deviation. However, the elements to compute the Sharpe ratio, namely, the expected returns and the volatilities are unknown numbers and need to be esti…
This paper aims at developing a new method by which to build a data-driven portfolio featuring a target risk-return. We first present a comparative study of recurrent neural network models (RNNs), including a simple RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) for selecting the best predictor to u…