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We forecast future volatilities and correlations of financial markets based on the current trends in these markets. This complements previous work that models future expected returns by a cubic polynomial of the current trend strength. Empirically, we observe that volatilities and correlations tend to increase day after day in times of strong up- or down-trends. This effect is particularly pronounced in down-trends. …
Volatility-based trading strategies have attracted a lot of attention in financial markets due to their ability to capture opportunities for profit from market dynamics. In this article, we propose a new volatility-based trading strategy that combines statistical analysis with machine learning techniques to forecast stock markets trend. The method consists of several steps including, data exploration, correlation and…
This paper derives an optimal portfolio that is based on trend-following signal. Building on an earlier related article, it provides a unifying theoretical setting to introduce an autocorrelation model with the covariance matrix of trends and risk premia. We specify practically relevant models for the covariance matrix of trends. The optimal portfolio is decomposed into four basic components that yield four basic por…
In this survey, a short introduction in the recent discovery of log-normally distributed market-technical trend data will be given. The results of the statistical evaluation of typical market-technical trend variables will be presented. It will be shown that the log-normal assumption fits better to empirical trend data than to daily returns of stock prices. This enables to mathematically evaluate trading systems depe…
Based on our "finance-prediction-oriented" methodology which involves such elements as log-periodic self-similarity, the universal preferred scaling factor lambda=2, and allows a phenomenon of the "super-bubble" we analyze the 2009 world stock market (here represented by the SP500, Hang Seng and WIG) development. We identify elements that indicate the third decade of September 2009 as a time limit for the present bul…
The primary objective of this paper is to conceive and develop a new methodology to detect notable changes in liquidity within an order-driven market. We study a market liquidity model which allows us to dynamically quantify the level of liquidity of a traded asset using its limit order book data. The proposed metric holds potential for enhancing the aggressiveness of optimal execution algorithms, minimizing market i…
Traditional risk-adjusted returns, such as the Treynor, Sharpe, Sortino, and Information ratios, have been pivotal in portfolio asset allocation, focusing on minimizing risk while maximizing profit. Nevertheless, these metrics often fail to account for the distinct characteristics of bull and bear markets, leading to sub-optimal investment decisions. This paper introduces a novel approach called the Market-adaptive R…
The cross-currency basis, which is the basis spread added mainly to the U.S. dollar London Interbank Offered Rate (USD LIBOR) when the USD is funded via foreign exchange (FX) swaps using the Japanese yen or the euro as a funding currency, has been widening globally since the beginning of 2014. This development is driven by (1) increased demands for U.S. dollars resulting from a divergence in the monetary policy betwe…
This paper studies how market informedness affects market makers' profitability in a computational market environment with heterogeneous learning agents. We develop an agent-based market model in which market makers differ in their information sets and inventory-risk aversion, prices form endogenously, fundamental values evolve exogenously, and market-taker order flow follows a state-dependent self-exciting process. …
The stock market is a crucial component of the financial market, playing a vital role in wealth accumulation for investors, financing costs for listed companies, and the stable development of the national macroeconomy. Significant fluctuations in the stock market can damage the interests of stock investors and cause an imbalance in the industrial structure, which can interfere with the macro level development of the …
Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning approaches has a number of limitations. The development of deep reinforcement learning techniques is partially addressing these issues. Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impe…
In recent years, a wide range of investment models have been created using artificial intelligence. Automatic trading by artificial intelligence can expand the range of trading methods, such as by conferring the ability to operate 24 hours a day and the ability to trade with high frequency. Automatic trading can also be expected to trade with more information than is available to humans if it can sufficiently conside…
Rather than directly predicting future prices or returns, we follow a more recent trend in asset management and classify the state of a market based on labels. We use numerous standard labels and even construct our own ones. The labels rely on future data to be calculated, and can be used a target for training a market state classifier using an appropriate set of market features, e.g. moving averages. The constructio…
The imbalance of buying and selling functions profoundly in the formation of market trends, however, a fine-granularity investigation of the imbalance is still missing. This paper investigates a unique transaction dataset that enables us to inspect the imbalance of buying and selling on the man-times level at high frequency, what we call 'trading polarity', for a large cross-section of stocks from Shenzhen Stock Exch…
Regarding the intraday sequence of high frequency returns of the S&P index as daily realizations of a given stochastic process, we first demonstrate that the scaling properties of the aggregated return distribution can be employed to define a martingale stochastic model which consistently replicates conditioned expectations of the S&P 500 high frequency data in the morning of each trading day. Then, a more general fo…
Portfolio allocation with gross-exposure constraint is an effective method to increase the efficiency and stability of selected portfolios among a vast pool of assets, as demonstrated in Fan et al (2008). The required high-dimensional volatility matrix can be estimated by using high frequency financial data. This enables us to better adapt to the local volatilities and local correlations among vast number of assets a…
Artificial transaction generation remains an important source of potential market manipulation on cryptocurrency exchanges, as it may distort reported liquidity and reduce market transparency. This study proposes a diagnostic framework for detecting unusual trading patterns based on complexity and statistical-structure measures derived from high-frequency trade-level data. The analysis considers log-returns, trading …
We test whether five widely promoted retail signal families - trend, oscillator, candlestick, volume, and calendar rules - deliver a positive, economically meaningful, net-of-cost, and survivable edge. Practical viability is the conjunction of three predeclared gates: statistical edge after multiplicity correction, economic viability after trading costs, and finite-bankroll survival under leverage. Exposure-matched b…
Industrial Production — Physical output trends that confirm or contradict financial market cyclical narratives.
Gamma Hedging — Delta adjustments by dealers that can accelerate trends or pin prices near strikes.
Pyramiding (Equity).
Commodity CTA Trend — Systematic trend following concentrated in commodity futures.
Productivity Trend US (Economy).
Productivity Trend Euro Area (Economy).
Productivity Trend UK (Economy).
Productivity Trend Japan (Economy).
Productivity Trend China (Economy).
Productivity Trend EM Asia (Economy).
Productivity Trend LatAm (Economy).
Productivity Trend CEEMEA (Economy).
Productivity Trend Canada (Economy).
Productivity Trend Australia (Economy).
Productivity Trend Nordics (Economy).
Productivity Trend ASEAN (Economy).
Productivity Trend India (Economy).
Productivity Trend Korea (Economy).
Productivity Trend Brazil (Economy).
Productivity Trend Mexico (Economy).
Productivity Trend South Africa (Economy).
Productivity Trend Turkey (Economy).
Productivity Trend Poland (Economy).
Productivity Trend Indonesia (Economy).
Productivity Trend headline (Economy).
Productivity Trend core (Economy).
Productivity Trend real-time (Economy).
Productivity Trend revision (Economy).
Productivity Trend surprise (Economy).
Commodity CTA Trend — Systematic trend following concentrated in commodity futures.
Gamma Hedging — Delta adjustments by dealers that can accelerate trends or pin prices near strikes.
Industrial Production — Physical output trends that confirm or contradict financial market cyclical narratives.
Productivity Trend ASEAN (Economy).
Productivity Trend Australia (Economy).
Productivity Trend Brazil (Economy).
Productivity Trend Canada (Economy).
Productivity Trend CEEMEA (Economy).
Productivity Trend China (Economy).
Productivity Trend core (Economy).
Productivity Trend EM Asia (Economy).
Productivity Trend Euro Area (Economy).
Productivity Trend headline (Economy).
Productivity Trend India (Economy).
Productivity Trend Indonesia (Economy).
Productivity Trend Japan (Economy).
Productivity Trend Korea (Economy).
Productivity Trend LatAm (Economy).
Productivity Trend Mexico (Economy).
Productivity Trend Nordics (Economy).
Productivity Trend Poland (Economy).
Productivity Trend real-time (Economy).
Productivity Trend revision (Economy).
Productivity Trend South Africa (Economy).