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.
Study finds inefficiency in Brazilian stock market through correlations and fat-tailed returns.
Study finds many stocks in S&P 500 are inefficient, suggesting financial analysts outperform blindfolded monkeys.
Study confirms Indian stock market is weak form inefficient.
The Moscow Stock Exchange was inefficient for most of 2012-2021.
Model shows stock markets can be inefficiently mispriced.
Analyzed Indian stock market data to find stylized facts with deviations.
In this paper we examine inefficiencies and information disparity in the Japanese stock market. By carefully analysing information publicly available on the internet, an `outsider' to conventional statistical arbitrage strategies--which are based on market microstructure, company releases, or analyst reports--can never…
How an investor invests in the market is largely influenced by the market efficiency because if a market is efficient, it is extremely difficult to make excessive returns because in an efficient market there will be no undervalued securities i.e. securities whose value is less than its assumed intrinsic value, which of…
We utilize long-term memory, fractal dimension and approximate entropy as input variables for the Efficiency Index [Kristoufek & Vosvrda (2013), Physica A 392]. This way, we are able to comment on stock market efficiency after controlling for different types of inefficiencies. Applying the methodology on 38 stock marke…
p-index approach shows efficient-contrarian strategy outperforms others in low-sentiment periods
We introduce a new measure for the capital market efficiency. The measure takes into consideration the correlation structure of the returns (long-term and short-term memory) and local herding behavior (fractal dimension). The efficiency measure is taken as a distance from an ideal efficient market situation. Methodolog…
We argue that an important contributing factor into market inefficiency is the lack of a robust mechanism for the stock price to rise if a company has good earnings, e.g., via buybacks/dividends. Instead, the stock price is prone to volatility due to rather random perception/interpretation of earnings announcements (am…
It has been assumed that arbitrage profits are not possible in efficient markets, because future prices are not predictable. Here we show that predictability alone is not a sufficient measure of market efficiency. We instead propose to measure inefficiencies of markets in terms of the maximal profit an ideal trader can…
We presented Bayesian portfolio selection strategy, via the factor asset pricing model. If the market is information efficient, the proposed strategy will mimic the market; otherwise, the strategy will outperform the market. The strategy depends on the selection of a portfolio via Bayesian multiple testing methodol…
Deep learning model predicts stock price movements based on historical data.
Increase Alpha uses deep learning to predict stock movements efficiently.
Using a two-point correlation technique, we study emergence of market efficiency in the emergent Russian futures market by focusing on lagged correlations. The correlation strength of leader-follower effects in the lagged inter-market correlations on the hourly time frame is seen to be significant initially (2009-2011)…
Study finds cryptoasset markets inefficient due to capital reallocation frictions.
Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in 2016 from the vantage point of a single and fixed frame of reference. We find that inefficiencies created in part by the fragmentation of the equity mark…
In this paper we seek to demonstrate the predictability of stock market returns and explain the nature of this return predictability. To this end, we introduce investors with different investment horizons into the news-driven, analytic, agent-based market model developed in Gusev et al. (2015). This heterogeneous frame…
Using the most comprehensive, commercially-available dataset of trading activity in U.S. equity markets, we catalog and analyze quote dislocations between the SIP National Best Bid and Offer (NBBO) and a synthetic BBO constructed from direct feeds. We observe a total of over 3.1 billion dislocation segments in the Russ…
Algorithm beats sports betting markets, showing inefficiencies.
Study minimizes market inefficiency in systemic economies.
Proposes a framework to predict stock movements by integrating multi-order and internal dynamics.
New study finds day-of-the-week effects in stock market returns using multifractal analysis.
Study finds market inefficiencies vary by time scale, with news uncertainty key.
Study reveals inefficiencies in EU carbon trading market.
Study finds Bitcoin market efficient, no exploitable inefficiencies with neural networks.
Paper tests for time-varying entropy in stock prices, finding periods of inefficiency.
It is customary that when security prices fully reflect all available information, the markets for those securities are said to be efficient. And if markets are inefficient, investors can use available information ignored by the market to earn abnormally high returns on their investments. In this context this paper tri…
For the London Stock Exchange we demonstrate that the signs of orders obey a long-memory process. The autocorrelation function decays roughly as with , corresponding to a Hurst exponent . This implies that the signs of future orders are quite predictable from the signs of past orde…
Investors trade based on shifting prices, leading to market inefficiencies.
Social learning can make financial markets inefficient, but individual learning can fix this.
I summarize the recent work on market (in)efficiency, highlighting key elements why financial markets will never be made efficient. My approach is not by adding more empirical evidence, but giving plausible reasons as to where inefficiency arises and why it's not rational to arbitrage it away.
New estimator reveals intraday betas mainly driven by correlations.
Market inefficiencies persist in DEXes, especially during high volatility.
The study assesses how financial markets' efficiency changed during the COVID-19 crisis.
LLMs show potential for predicting financial returns, contrary to common belief.
Passive investing can incur hidden costs due to market timing inefficiencies.
We propose a new set of stylized facts quantifying the structure of financial markets. The key idea is to study the combined structure of both investment strategies and prices in order to open a qualitatively new level of understanding of financial and economic markets. We study the detailed order flow on the Shenzhen …
This paper examines Bitcoin's price predictability, finding inefficiencies under certain conditions.
Study analyzes EU ETS carbon market dynamics, revealing inefficiencies and anomalies.
Study tests financial market efficiency using random number generator tests.
Study reveals the 2020 U.S. stock crash was endogenous, not caused by COVID.
It seems to be very unlikely that all relevant information in the stock market could be fully encoded in a geometrical shape. Still,the present paper will reveal the geometry behind the stock market transactions. The prices of market index (DJIA) stock components are arranged in ascending order from the smallest one in…
Smart beta, also known as strategic beta or factor investing, is the idea of selecting an investment portfolio in a simple rule-based manner that systematically captures market inefficiencies, thereby enhancing risk-adjusted returns above capitalization-weighted benchmarks. We explore the idea of applying a smart strat…
A simple and elegant arrangement of stock components of a portfolio (market index-DJIA) in a recent paper [1], has led to the construction of crossing of stocks diagram. The crossing stocks method revealed hidden remarkable algebraic and geometrical aspects of stock market. The present paper continues to uncover new ma…
KAN-PCA improves asset return analysis by capturing more variance than classical PCA during market crises.