Featured and Latest Posts
What Does a Good Financial Decision Look Like?
Good financial decisions aren’t about predicting the future—they’re about following a sound process today.
In investing, outcomes are noisy. Short-term performance often reflects randomness, not skill. Yet fund managers continue to pitch five-year track records as if they prove anything. They don’t.
As Ken French puts it, a five-year chart ‘tells you nothing’. The real skill lies in filtering out the noise—evaluating strategy, incentives, costs, and behavioural fit.
Don’t chase what worked recently. Stick with what works reliably.
Home Bias in Asset Allocation: A Rational Case for (Some) UK Overexposure?
Home bias—the tendency to favour domestic assets—is one of the most persistent patterns in investor behaviour. For UK investors, that means holding more in UK equities, gilts, property and sterling assets than global market capitalisation would suggest. And whilst much of this is driven by familiarity or inertia, a blanket rejection of UK exposure may go too far.
This post examines the case for a modest UK overweight, exploring five rational reasons why some home bias may actually be optimal. From currency matching and tax efficiency to relative valuations and implementation simplicity, there are good reasons for UK investors to hold 15–25% of their equity allocation at home—deliberately, not by default.
Is Factor Investing Worthwhile Pursuing? Practically speaking?
Factor investing promises a scientific edge—systematic returns from tilting towards characteristics like value, size, or momentum. But that promise is under review. Economist Andrew Chen, co-creator of the Open Source Asset Pricing project, casts doubt on the viability of many factors, especially for retail investors.
Chen argues that much of the language around ‘factors’ is imprecise. Many so-called factors are merely statistical predictors, not true sources of systematic return. Even when predictors are statistically sound, their effectiveness often fades after publication—and real-world trading costs can erase any remaining edge.
The takeaway? Whilst a small number of robust, low-cost, multi-factor strategies—like those from Dimensional, AQR, or Vanguard—may still offer modest rewards, the broader field of factor investing appears less fertile than it once seemed. For most investors, the global market index remains a formidable benchmark.
Who Really Determines Stock Prices? The Surprising Influence of Retail Investors
Traditional theory holds that stock prices reflect fundamental value, adjusting efficiently as rational investors incorporate new information. But recent research by Ralph Koijen and others turns this view on its head. Their findings reveal that markets are far more sensitive to shifts in investor demand than standard models predict—and retail investors play a much bigger role than previously assumed.
In theory, buying 1 per cent of a stock should move the price by just 0.02 per cent. In reality, Koijen shows it moves by roughly 1 per cent—a 5,000-fold difference. The reason? Demand is far more inelastic than standard models assume. And when Koijen examines who is actually driving this volatility, he finds that retail investors and smaller institutions—not large institutions—are the key contributors to cross-sectional price movements.
This inelasticity has profound implications. Prices can deviate significantly from fundamentals for prolonged periods, and arbitrage is often too constrained to correct them quickly. The result is a market that may remain efficient over the long run—but behaves far more irrationally, noisily, and sentimentally in the short term than traditional finance theory would suggest.
Good financial decisions aren’t about predicting the future—they’re about following a sound process today.
In investing, outcomes are noisy. Short-term performance often reflects randomness, not skill. Yet fund managers continue to pitch five-year track records as if they prove anything. They don’t.
As Ken French puts it, a five-year chart ‘tells you nothing’. The real skill lies in filtering out the noise—evaluating strategy, incentives, costs, and behavioural fit.
Don’t chase what worked recently. Stick with what works reliably.