Central bank behavior and gold trading

 

Gold has had a complete turnaround after reaching highs well above $5,000 an ounce. So, what was going on? One clear indicator is that central banks have cut their gold purchases. Central banks have not been viewed as profit maximizers, but the high prices in the first quarter may have been too much, and they took a step back from the market. The other profit-maximizers may have had the same view, and the result has been an over 25% decline from the highs in just over one quarter. This has occurred with both a Middle East war and continued inflation. 

While real-time buying by central banks is hard to obtain, the demand for gold by different agents is still an important way to track gold dynamics.  

The failure of forward guidance or what should we expect

 

Analysts are calling it a failure of the Fed's new forward guidance policy. The policy, of course, is no forward guidance. Given Fed Chairman Warsh is not going to tell the market much about the intention of Fed policy other than "it will not waver" from trying to fight inflation, the market will have to decide on the efficacy of current policy. The Fed, instead of driving policy, can now learn what the markets are discounting. The market is now providing information to the Fed instead of the Fed trying to manipulate the direction of rates. 

Prices and markets are acting as signals, not as something that is controlled by central bank guidance. Now, the Fed and the market may not like the signal,  but they are signals nevertheless. The signals are now very clear. The market does not believe that the Fed can control inflation, especially over the longer run. Long rates are reaching year highs, and the yield curve is steepening. This signal, especially with the increase in real rates, is tightening the credit markets. 

The bond markets are now once again a signaling market. There will be more uncertainty in these markets, but they are now telling the Fed what they think about policy, both monetary and fiscal. You may not like what the markets is telling us, but the message is clear.

How to measure slippage - look for negative surprises

 

The paper by Ilija I. Zovko, "Realtime price impact detection"  provides an interesting solution to the slippage monitoring problem. 

Traditional methods for managing market impact typically rely on monitoring post-fill price slippage. This approach fails in real-time environments for two key reasons:

Statistically Slow - Estimating price slippage requires hundreds of fills before the signal can be statistically distinguished from background market volatility. By the time a trader detects impact through slippage, the order execution is usually finished.

Causally Ambiguous - Slippage measures correlation, not causality. An adverse price movement could be caused by the trader’s own actions (information leakage) or by an unrelated participant/market alpha. The required response is opposite in each case: slow down trading if caused by self-impact, or speed up trading if competing against outside alpha.

Instead of tracking price level changes over time, Zovko proposes tracking timing synchronicity.

The model tests for statistical surprise in how quickly an adverse market event occurs immediately following a trader's action (a fill or limit placement). The underlying assumption is that when a trader's actions leak information or cause market impact, the market reacts unusually fast. Unusually fast adverse market prints serve as a signature of causal impact.

To turn timing surprise into an actionable real-time metric, the research establishes a two-step statistical approach:

First, the background stream of market events is modeled as a Poisson process with a locally estimated, time-varying intensity. Each post-action event yields a $p$-value quantifying how surprising it is to observe an adverse event so rapidly.

Second, because a single fast event is insufficient proof, the p-values across successive fills are aggregated using Fisher's method. This allows statistically significant evidence to accumulate rapidly.

The resulting statistic reacts within a handful of fills (e.g., 3–5 actions) rather than requiring hundreds of fills. Because it operates in real time and detects causality, traders can dynamically throttle exposure to specific venues, counterparties, or execution styles mid-order if impact is detected.

Momentum and network learning

The paper “Network Momentum across Asset Classes” introduces a multi-asset quantitative trading strategy based on network momentum—a trading signal derived from momentum spillover across different asset classes. Momentum spillover occurs when past performance in one asset predicts future returns in linked assets. While traditionally studied in pairwise connections (e.g., stock-bond pairs or supply chains), this paper expands the concept to a systemic network across four distinct asset classes.

The study analyzes daily price data from 1990 to 2022 across 64 continuous futures contracts covering Commodities, Equities, Fixed Income (FI), and Foreign Exchange (FX).

Methodology

Graph Learning - Uses a linear and interpretable graph learning model to infer dynamic asset networks strictly from historical price data, overcoming the lack of explicit fundamental links between disparate asset classes.

Momentum Features - Leverages 8 individual momentum features per asset, including volatility-scaled returns over multiple horizons (1-day to 1-year) and normalized Moving Average Convergence Divergence (MACD) indicators.

Portfolio Construction - Combines learned graph adjacency matrices with a linear regression model to forecast 1-day volatility-scaled returns across all assets.

Out-of-sample backtesting from 2000 to 2022 yields an annualized return of 22% and a Sharpe ratio of 1.51 after volatility scaling. The strategy exhibits low downside risk and low correlation with traditional individual momentum strategies. Inter-class (cross-asset) connections significantly enhance return predictability and diversification.