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.
Investment risk on a regulated market is influenced by gold prices and oil trading.
Investment strategies involving cryptocurrencies and VIX INDEX show positive impact in market performance.
ChatGPT improves financial reasoning, overcoming biases in gold investment.
The study improves gold's role as a hedge and safe haven using new correlation measures.
We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations across a number of investmen…
This article continues our analysis of the gold price dynamics that was published in December 2010 (abs/1012.4118) and forecasted the possibility of the "burst of the gold bubble" in April - June 2011. Our recent analysis suggests the possibility of one more substantial fluctuation before the final collapse in July 201…
Investments with best performance are not associated with best Sharpe ratios.
This paper studies the empirical tracking performance of leveraged ETFs on gold, and their price relationships with gold spot and futures. For tracking the gold spot, we find that our optimized portfolios with short-term gold futures are highly effective in replicating prices. The market-traded gold ETF (GLD) also exhi…
This paper provides an innovative perspective on the role of gold as a hedge and safe haven. We use a quantile-on-quantile regression approach to capture the dependence structure between gold returns and changes in uncertainty under different gold market conditions, while considering the nuances of uncertainty levels. …
Gold prices show seasonal behavior, with January and July having opposite returns.
Cryptocurrency prices predicted using LSTM, SVM, and polynomial regression.
Crypto-assets perform better than gold as safe-havens during market crashes.
The paper analyzes gold, oil, and bitcoin futures volatility and basis.
Improved cGANs using GOLD measure for better data distribution alignment.
Gold and currency markets form a unique pair with specific interactions and dynamics. We focus on the efficiency ranking of gold markets with respect to the currency of purchase. By utilizing the Efficiency Index (EI) based on fractal dimension, approximate entropy and long-term memory on a wide portfolio of 142 gold p…
This paper analyzes the direction of the causality between crude oil, gold and stock markets for the largest economy in the world with respect to such markets, the US. To do so, we apply non-linear Granger causality tests. We find a nonlinear causal relationship among the three markets considered, with the causality go…
We show that the behaviour of Bitcoin has interesting similarities to stock and precious metal markets, such as gold and silver. We report that whilst Litecoin, the second largest cryptocurrency, closely follows Bitcoin's behaviour, it does not show all the reported properties of Bitcoin. Agreements between apparently …
Study examines impact of oil and gold prices on Tehran Stock Exchange.
We give emphasis on the use of chaos-based rigorous nonlinear technique called Visibility Graph Analysis, to study one economic time series - gold price of USA. This method can offer reliable results with fiinite data. This paper reports the result of such an analysis on the times series depicting the fluctuation of go…
This working paper analyzes the gold price dynamics on the basis of methodology developed by Didier Sornette. Our calculations indicate that this dynamics is close to the one of the "bubbles" studied by Sornette and that the most probable timing of the "burst of the gold bubble" is April - June 2011. The obtained resul…
Achilles predicts Gold vs USD with a profitable trading bot.
We devise a USDCHF trading strategy using the dynamics of gold as a filter. Our strategy involves modelling both USDCHF and gold using a coupled hidden Markov model (CHMM). The observations will be indicators, RSI and CCI, which will be used as triggers for our trading signals. Upon decoding the model in each iteration…
This non-linear relationship in the joint time-frequency domain has been studied for the Indian National Stock Exchange (NSE) with the international Gold price and WTI Crude Price being converted from Dollar to Indian National Rupee based on that week's closing exchange rate. Though a good correlation was obtained duri…
Study reduces financial dynamics complexity using PCA for NASDAQ, oil, gold, and USD.
Forecast-to-fill strategy generates durable alpha in gold futures.
Introduces a new stationary GE-process for gold price analysis.
PPI++ outperforms gold-standard labels only if pseudo-labels are highly correlated.
Meta-Router optimizes LLM selection using gold-standard and preference-based data.
Bi-LSTM with attention predicts gold and bitcoin prices accurately.
Our study proposes a new currency system to protect wealth from over-issued fiat and stablecoins.
Long-range correlation and fluctuation in the gold market time series of world's two leading gold consuming countries, namely China and India, are studied. For both the market series during the period 1985-2013 we observe a long-range persistence of memory in the sequences of maxima (minima) of returns in successive ti…
Unified framework for learning with indirect supervision signals.
Improved MF-DFA model analyzes precious metals market efficiency and multifractality.
ETF approval boosts Bitcoin's correlation with equities, stabilizes with gold, and maintains negative correlation with fiat currencies.
This study compares Bitcoin and Litecoin using cryptocurrency metrics and trading strategies.
The multifractal detrended fluctuation analysis technique is employed to analyze the time series of gold consumer price index (CPI) and the market trend of three world's highest gold consuming countries, namely China, India and Turkey for the period: 1993-July 2013. Various multifractal variables, such as the generaliz…
The receiver operating characteristic (ROC) curve is a very useful tool for analyzing the diagnostic/classification power of instruments/classification schemes as long as a binary-scale gold standard is available. When the gold standard is continuous and there is no confirmative threshold, ROC curve becomes less useful…
Study of the forecasting models using large scale microblog discussions and the search behavior data can provide a good insight for better understanding the market movements. In this work we collected a dataset of 2 million tweets and search volume index (SVI from Google) for a period of June 2010 to September 2011. We…
This paper clarifies Bitcoin's volatility and predictability across daily, weekly, and monthly scales.
Efficiently evaluate generative models at the prompt level using tensor factorization.
New method improves statistical inference using machine learning-imputed data.
We propose that imitation between traders and their herding behaviour not only lead to speculative bubbles with accelerating over-valuations of financial markets possibly followed by crashes, but also to ``anti-bubbles'' with decelerating market devaluations following all-time highs. For this, we propose a simple marke…
We propose a mathematical procedure for finding informed traders in ultra-high frequency trading. We wrote it as Vector ARMA and found condition of its stationarity. For the price exposure complied with ARMA(1,2) we proved that underlying asset price difference can be derived as ARMA(1,1) process. For validation of the…
Study characterizes and mitigates imbalances in neurosymbolic learning.
Study compares Bitcoin, gold, and gas price complexity using multifractal and multiscale entropy methods.
This study examines whether tokenized assets improve liquidity and finds significant differences across categories.
Bitcoin fails to prove safe haven status during pandemic.
In semantic parsing for question-answering, it is often too expensive to collect gold parses or even gold answers as supervision signals. We propose to convert model outputs into a set of human-understandable statements which allow non-expert users to act as proofreaders, providing error markings as learning signals to…