Market Efficiency

Chance or Chaos? Fractal geometry aimed to inspect the nature of Bitcoin ArXiv ID: 2309.00390 “View on arXiv” Authors: Unknown Abstract The aim of this paper is to analyse the Bitcoin in order to shed some light on its nature and behaviour. We select 9 cryptocurrencies that account for almost 75% of total market capitalisation and compare their evolution with that of a wide variety of traditional assets: commodities with spot and futures contracts, treasury bonds, stock indices, growth and value stocks. Fractal geometry will be applied to carry out a careful statistical analysis of the performance of the Bitcoin returns. As a main conclusion, we have detected a high degree of persistence in its prices, which decreases the efficiency but increases its predictability. Moreover, we observe that the underlying technology influences price dynamics, with fully decentralised cryptocurrencies being the only ones to exhibit self-similarity features at any time scale. ...

Trend patterns statistics for assessing irreversibility in cryptocurrencies: time-asymmetry versus inefficiency ArXiv ID: 2307.08612 “View on arXiv” Authors: Unknown Abstract In this paper, we present a measure of time irreversibility using trend pattern statistics. We define the irreversibility index as the Kullback-Leibler divergence between the distribution of uptrends subsequences (increasing trends) and the corresponding downtrends subsequences distribution (decreasing trends) in a time series. We use this index to analyze the degree of irreversibility in log return series over time, specifically focusing on five cryptocurrencies: Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. Our analysis reveals a strong indication of irreversibility in all these cryptocurrencies and the characteristic evolves over time. We additionally evaluate the market efficiency for these cryptocurrencies based on a recently proposed information-theoretic measure. By comparing inefficiency and irreversibility, we explore the relationship between these statistical features. This comparison provides insight into the non-trivial relationship between inefficiency and irreversibility. ...

Integrating Different Informations for Portfolio Selection ArXiv ID: 2305.17881 “View on arXiv” Authors: Unknown Abstract Following the idea of Bayesian learning via Gaussian mixture model, we organically combine the backward-looking information contained in the historical data and the forward-looking information implied by the market portfolio, which is affected by heterogeneous expectations and noisy trading behavior. The proposed combined estimation adaptively harmonizes these two types of information based on the degree of market efficiency and responds quickly at turning points of the market. Both simulation experiments and a global empirical test confirm that the approach is a flexible and robust forecasting tool and is applicable to various capital markets with different degrees of efficiency. ...

Not feeling the buzz: Correction study of mispricing and inefficiency in online sportsbooks ArXiv ID: 2306.01740 “View on arXiv” Authors: Unknown Abstract We present a replication and correction of a recent article (Ramirez, P., Reade, J.J., Singleton, C., Betting on a buzz: Mispricing and inefficiency in online sportsbooks, International Journal of Forecasting, 39:3, 2023, pp. 1413-1423, doi: 10.1016/j.ijforecast.2022.07.011). RRS measure profile page views on Wikipedia to generate a “buzz factor” metric for tennis players and show that it can be used to form a profitable gambling strategy by predicting bookmaker mispricing. Here, we use the same dataset as RRS to reproduce their results exactly, thus confirming the robustness of their mispricing claim. However, we discover that the published betting results are significantly affected by a single bet (the “Hercog” bet), which returns substantial outlier profits based on erroneously long odds. When this data quality issue is resolved, the majority of reported profits disappear and only one strategy, which bets on “competitive” matches, remains significantly profitable in the original out-of-sample period. While one profitable strategy offers weaker support than the original study, it still provides an indication that market inefficiencies may exist, as originally claimed by RRS. As an extension, we continue backtesting after 2020 on a cleaned dataset. Results show that (a) the “competitive” strategy generates no further profits, potentially suggesting markets have become more efficient, and (b) model coefficients estimated over this more recent period are no longer reliable predictors of bookmaker mispricing. We present this work as a case study demonstrating the importance of replication studies in sports forecasting, and the necessity to clean data. We open-source release comprehensive datasets and code. ...

How Competitive is the Stock Market? Theory, Evidence from Portfolios, and Implications for the Rise of Passive Investing ArXiv ID: ssrn-3821263 “View on arXiv” Authors: Unknown Abstract The conventional wisdom in finance is that competition is fierce among investors: if a group changes its behavior, others adjust their strategies such that noth Keywords: Market Efficiency, Investor Behavior, Game Theory, Strategic Interaction, Equities Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper employs a semi-structural economic model with equilibrium conditions, endogenous elasticities, and formal estimation challenges (reflection problem, endogeneity), requiring advanced mathematics. It is empirically rigorous, using detailed institutional portfolio data and a novel identification strategy with instruments to estimate the demand system and the strategic response of investors. flowchart TD A["Research Goal: Quantify investor competition<br>and its implications for passive investing"] --> B["Methodology: Game-theoretic model<br>of strategic portfolio choice"] B --> C["Data: US equity market portfolios<br>1980-2015 (CRSP)"] C --> D["Computational Process:<br>Simulate competitive equilibria<br>under varying investor assumptions"] D --> E["Key Findings:<br>1. Competition is strong but incomplete<br>2. Passive investing reduces competition<br>3. Market efficiency varies with investor structure"]

Detection of False Investment Strategies Using Unsupervised Learning Methods ArXiv ID: ssrn-3167017 “View on arXiv” Authors: Unknown Abstract Most investment strategies uncovered by practitioners and academics are false. This partially explains the high rate of failure, especially among quantitative h Keywords: quantitative finance, investment strategies, backtesting bias, market efficiency, quantitative strategies Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 2.0/10 Quadrant: Lab Rats Why: The paper introduces a complex unsupervised learning algorithm involving probability distributions and multiple testing corrections, but lacks specific implementation details, code, or detailed backtesting results, focusing more on theoretical and statistical methodology. flowchart TD A["Research Goal:<br>Detect false quantitative investment strategies"] --> B["Methodology:<br>Unsupervised Learning (e.g., Clustering)"] B --> C["Data Inputs:<br>Strategy Returns, Factor Loadings, Backtest Metrics"] C --> D["Computational Process:<br>Identify Outliers & Anomalies in Strategy Space"] D --> E["Key Findings:<br>Strategies are often noise; high failure rate due to backtesting bias"]

The Impact of Dividend Policy on Share Price Volatility in the Malaysian Stock Market ArXiv ID: ssrn-2147458 “View on arXiv” Authors: Unknown Abstract The purpose of this study was to examine the relationship between dividend policy and share price volatility with a focus on consumer product companies listed i Keywords: dividend policy, share price volatility, consumer goods, market efficiency, equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The study employs standard regression analysis with limited mathematical complexity, and while it uses real market data from Malaysia, it lacks the code, detailed backtesting metrics, or implementation details typical of high-empirical rigor papers. flowchart TD A["Research Goal: Examine Dividend Policy Impact<br>on Share Price Volatility in Malaysian Equities"] --> B{"Methodology"} B --> C["Data Collection: Financial Statements &<br>Stock Prices (2015-2020)"] C --> D["Sample: Malaysian Consumer Product Companies"] D --> E{"Computational Processes"} E --> F["Regression Analysis: Fixed Effects Model"] F --> G["Variables: Dividend Yield, Payout Ratio,<br>Volatility Measures"] G --> H["Key Findings/Outcomes"] H --> I["Dividend Policy significantly reduces<br>Share Price Volatility"] H --> J["Supports Market Efficiency & Investor<br>Protection Hypotheses"]

Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis ArXiv ID: ssrn-1702447 “View on arXiv” Authors: Unknown Abstract The battle between proponents of the Efficient Markets Hypothesis and champions of behavioral finance has never been more pitched, and little consensus exists a Keywords: Efficient Market Hypothesis, Behavioral Finance, Market Efficiency, Asset Pricing, Equities Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper presents a high-level conceptual framework (Adaptive Markets Hypothesis) reconciling two established theories with minimal advanced mathematics, relying on qualitative arguments and evolutionary analogies rather than dense models or empirical backtesting. flowchart TD A["Research Goal:<br>Can markets be both<br>efficient and behavioral?"] --> B["Methodology:<br>AMH Framework<br>Adaptive Markets Hypothesis"] B --> C["Input Data:<br>Asset Pricing &<br>Equity Returns"] C --> D["Computation:<br>Event Studies &<br>Statistical Analysis"] D --> E["Key Finding:<br>Market Efficiency is<br>Not Static"] E --> F["Outcome:<br>Efficiency Varies by<br>Conditions & Competition"]

BehavioralFinance: An Introduction ArXiv ID: ssrn-1488110 “View on arXiv” Authors: Unknown Abstract This survey introduces and reviews the field of behavioral finance. It outlines the traditional finance approach, which builds upon rational acting investors, i Keywords: Behavioral Finance, Rational Investors, Cognitive Biases, Market Efficiency, General Finance Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: This paper is a high-level survey that discusses theoretical concepts and empirical anomalies without presenting new mathematical models or implementation details for backtesting. flowchart TD A["Research Goal:<br/>Review Behavioral Finance Foundations"] --> B["Methodology:<br/>Literature Survey & Framework Analysis"] B --> C["Data/Inputs:<br/>Traditional Finance Models<br/>Cognitive Bias Studies"] C --> D{"Computational Process:<br/>Rational vs. Behavioral Comparison"} D --> E["Key Finding 1:<br/>Investors often deviate from rationality"] D --> F["Key Finding 2:<br/>Cognitive biases impact markets"] D --> G["Key Finding 3:<br/>Market efficiency challenged"] E & F & G --> H["Outcome:<br/>Integrated Behavioral Finance Framework"]

Reconciling Efficient Markets with BehavioralFinance: The Adaptive Markets Hypothesis ArXiv ID: ssrn-728864 “View on arXiv” Authors: Unknown Abstract The battle between proponents of the Efficient Markets Hypothesis and champions of behavioral finance has never been more pitched, and there is little consensus Keywords: Efficient Market Hypothesis, Behavioral Finance, Market Efficiency, Asset Pricing, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.5/10 Quadrant: Philosophers Why: The paper is primarily a conceptual and theoretical synthesis of existing ideas (EMH vs. behavioral finance) using an evolutionary analogy, lacking novel mathematical derivations or heavy empirical backtesting. flowchart TD A["Research Goal:<br>Reconcile EMH with Behavioral Finance"] --> B["Methodology:<br>Empirical Asset Pricing Tests"] B --> C{"Data Inputs:<br>US Equities (CRSP/Compustat)"} C --> D["Computational Process:<br>Estimate Risk-Adjusted Returns"] D --> E{"Outcomes / Findings"} E --> F["Markets are adaptive<br>Efficiency evolves over time"] E --> G["Behavioral anomalies<br>arise from market shocks"] E --> H["Asset pricing models<br>must incorporate adaptiveness"]