Résultats de recherche pour « Prediction Markets »

The cross-correlations between price fluctuations of 201 frequently traded stocks in the National Stock Exchange (NSE) of India are analyzed in this paper. We use daily closing prices for the period 1996-2006, which coincides with the period of rapid transformation of the market following liberalization. The eigenvalue distribution of the cross-correlation matrix, $\mathbf{C}$, of NSE is found to be similar to that of developed markets, such as the New York Stock Exchange (NYSE): the majority of eigenvalues fall within the bounds expected for a random matrix constructed from mutually uncorrelated time series. Of the few largest eigenvalues that deviate from the bulk, the largest is identified with market-wide movements. The intermediate eigenvalues that occur between the largest and the bulk have been associated in NYSE with specific business sectors with strong intra-group interactions. However, in the Indian market, these deviating eigenvalues are comparatively very few and lie much closer to the bulk. We propose that this is because of the relative lack of distinct sector identity in the market, with the movement of stocks dominantly influenced by the overall market trend. This is shown by explicit construction of the interaction network in the market, first by generating the minimum spanning tree from the unfiltered correlation matrix, and later, using an improved method of generating the graph after filtering out the market mode and random effects from the data. Both methods show, compared to developed markets, the relative absence of clusters of co-moving stocks that belong to the same business sector. This is consistent with the general belief that emerging markets tend to be more correlated than developed markets.

Nearly 2,200 political prediction markets involving more than $3.2 billion in platform-reported trading volume (Confino, 2024) were traded across the Iowa Electronic Markets, Kalshi, PredictIt, and Polymarket during the final nine weeks of the 2024 U.S. presidential campaign. Overall, 86% of these actively traded political markets performed better than a coin-flip, and they were also generally well-calibrated. Even so, there was modest evidence of longshot bias, and the impressive topline accuracy partly reflects the composition of the contracts being traded. 66% of all markets were priced below $0.20 or above $0.80 on the eve of Election Day, and 96% of those contracts resolved as priced. Among markets priced between $0.40 and $0.60 (12% of the sample), 55% resolved in favor of the priced favorite. Market efficiency was more mixed. Prices involving related political events generally covaried, but daily price changes were considerably less synchronized than price levels — suggesting that short-run movements were often market-specific. Arbitrage opportunities in presidential winner-take-all markets were also present on nearly every day. The prices of political prediction markets may provide useful signals about expected outcomes overall, but aggregate evidence of past accuracy and calibration neither guarantees future market performance nor implies that the price of any particular contract provides a definitive or fully efficient assessment of political reality.

The nature of fluctuations in the Indian financial market is analyzed in this paper. We have looked at the price returns of individual stocks, with tick-by-tick data from the National Stock Exchange (NSE) and daily closing price data from both NSE and the Bombay Stock Exchange (BSE), the two largest exchanges in India. We find that the price returns in Indian markets follow a fat-tailed cumulative distribution, consistent with a power law having exponent $α\sim 3$, similar to that observed in developed markets. However, the distributions of trading volume and the number of trades have a different nature than that seen in the New York Stock Exchange (NYSE). Further, the price movement of different stocks are highly correlated in Indian markets.

<p>The Financial Markets Conduct Act 2013 (FMC Act) represents the most substantial overhaul of New Zealand’s securities law in recent history. The regulation of derivatives in particular featured high on the agenda as an area in need of reform and, as a result, the FMC Act is much clearer than the Securities Markets Act 1988 with respect to typical derivative agreements. The focus of this paper, however, is on the atypical: the use of derivatives in prediction markets. With a study of New Zealand-based prediction market iPredict, this paper examines whether iPredict will be regulated under the FMC Act and, if so, how it will be regulated. The conclusion reached is that iPredict can operate under the FMC Act only if the Financial Markets Authority (FMA) declares that its contracts are derivatives and grants substantial exemptions from regulatory compliance. This paper then makes recommendations for a more coherent approach to the regulation of prediction markets under the FMC Act.</p>

A market model in Stochastic Portfolio Theory is a finite system of strictly positive stochastic processes. Each process represents the capitalization of a certain stock. If at any time no stock dominates almost the entire market, which means that its share of total market capitalization is not very close to one, then the market is called diverse. There are several ways to outperform diverse markets and get an arbitrage opportunity, and this makes these markets interesting. A feature of real-world markets is that stocks with smaller capitalizations have larger drift coefficients. Some models, like the Volatility-Stabilized Model, try to capture this property, but they are not diverse. In an attempt to combine this feature with diversity, we construct a new class of market models. We find simple, easy-to-test sufficient conditions for them to be diverse and other sufficient conditions for them not to be diverse.

Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation. Unfortunately, without segmentation masks, these methods will take the whole image as input, such that makes them difficult to focus on tumor regions and potentially unable to fully leverage the prognostic information within the tumor regions. In this study, we propose a radiomics-enhanced deep multi-task framework for outcome prediction from PET/CT images, in the context of HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR 2022). In our framework, our novelty is to incorporate radiomics as an enhancement to our recently proposed Deep Multi-task Survival model (DeepMTS). The DeepMTS jointly learns to predict the survival risk scores of patients and the segmentation masks of tumor regions. Radiomics features are extracted from the predicted tumor regions and combined with the predicted survival risk scores for final outcome prediction, through which the prognostic information in tumor regions can be further leveraged. Our method achieved a C-index of 0.681 on the testing set, placing the 2nd on the leaderboard with only 0.00068 lower in C-index than the 1st place.

<p><span>This Essay provides a unique legal analysis of the rise of prediction platforms such as Kalshi and Polymarket. Due to the recency of commercialized, publicly accessible prediction markets, they are only just now beginning to receive attention in the legal scholarship. Most of the early academic commentary on the subject, however, takes a highly normative slant. The focus is on the costs-and-benefits to society of prediction markets, often through a skeptical lens that emphasizes dysfunctional aspects of prediction markets such as insider trading scandals. By contrast, this essay is agnostic on whether prediction markets are a good or bad thing, and instead examines the particular legal environment that prominent prediction platforms have managed to carve out in order to achieve their booming popularity. That analysis highlights the central role of regulatory arbitrage in shaping the legal landscape of prediction markets, along with the political economy dynamics underlying the contested and still evolving regulatory framework that governs those markets.</span></p>

Standard approaches to the theory of financial markets are based on equilibrium and efficiency. Here we develop an alternative based on concepts and methods developed by biologists, in which the wealth invested in a financial strategy is like the abundance of a species. We study a toy model of a market consisting of value investors, trend followers and noise traders. We show that the average returns of strategies are strongly density dependent, i.e. they depend on the wealth invested in each strategy at any given time. In the absence of noise the market would slowly evolve toward an efficient equilibrium, but the statistical uncertainty in profitability (which is adjusted to match real markets) makes this noisy and uncertain. Even in the long term, the market spends extended periods of time away from perfect efficiency. We show how core concepts from ecology, such as the community matrix and food webs, give insight into market behavior. The wealth dynamics of the market ecology explain how market inefficiencies spontaneously occur and gives insight into the origins of excess price volatility and deviations of prices from fundamental values.

An event contract settles at one dollar or zero, so its price is a probability and the exchange is a market quoted in the currency of Bayes. We give a Bayesian account of trading such contracts, with the CFTC regulated exchange Kalshi as the leading case. Three ingredients are needed. First, a model whose latent parameter is low dimensional relative to the number of listed contracts, so that a few liquid quotes identify the whole board. Second, a posterior that carries parameter uncertainty into every derived contract rather than a plug in estimate. Third, a sizing rule. We show that Kelly betting on an exclusive family of contracts has an explicit water filling solution with a shadow price b that is strictly below one, so that contracts with negative naive expected value can enter the growth optimal portfolio as hedges, and that the optimal growth rate is i∈S p i log(p i /c i) + (1-p S) log b, which collapses to the Kullback-Leibler divergence KL(p∥c) when the book is fair. The Harville (1973) formula is treated as the canonical low dimensional generator of a combinatorially large contract set, with a Jensen argument showing that plug in exacta prices are biased downward. We recall the Skellam calibration of real time soccer odds of Feng et al. (2016) and the model free martingale diagnostics of Aldous (2013), whose halftime price and serious candidates principles give tests of a Kalshi board that require no model at all. Kalshi's fee θπ(1-π) with θ = 0.07 creates a no trade band of width 2θπ(1-π), at most 3.5 cents at the mid, and it changes which positions exist and not merely their size. An ϵ contamination prior that shrinks the posterior toward the price scales the Kelly stake by exactly 1-ϵ, which makes fractional Kelly a Bayes rule rather than a heuristic, and a simulation study shows that the shrinkage is worth roughly six times its cost once the trader's input is noisy rather than merely uncertain. For the maker side we derive the adverse selection spread on a binary contract, which is 4µp(1-p) for an informed order flow fraction µ and therefore has the same quadratic shape as the exchange fee, and we show that a log utility maker's inventory adjusted reservation price is p i /(ΛW i), falling directly out of the same first order conditions. Three real episodes anchor the framework: the 2025 federal shutdown duration ladder, whose twelve rungs are reproduced to within a cent by a one parameter hazard; the 2024 election board and its cross venue incoherence; and the favourite longshot bias documented on Kalshi by Bürgi et al. (2025), under which contracts below ten cents lose more than sixty percent of the money staked.