Why It’s Difficult

Today, you can feel like you need a maths degree just to keep up with the tsunami of numbers. Investment analysis demands deeper skills than traditional training provides.?

Relying on flawed metrics or misunderstood data can lead to costly mistakes.

Knowing when to partner with specialists can improve results.

www.teamassetmanagement.com?

The Overload

• Turning the endless figures, statistics and raw data into useful insights isn’t always straightforward.

And

• Misinterpreted data can lead to costly mistakes or failed investments.

Sampling, Data, Survivorship and Time Bias

• Equity indices like the FTSE 100 or S&P 500 are samples, not the full stock market. Witness effect of "Mag 7".
• Data Mining Bias: Interrogating data until a pattern appears - ?risks identifying a false trend.
• Survivorship Bias:?Historical performance excluding failed or delisted srtocks/funds, inflating reported returns.
• Time Period Bias:?“Make the numbers look good” - Using specific periods can distort results - ?better than they are.

The Bloody Sharpe Ratio

• Excess return over the risk-free rate, divided by standard deviation.
• Oversimplifies risk.

Buying a car using a Sharpe-like ratio:

• Return:?Satisfaction and utility.
• Risk:?Costs - maintenance, insurance, and depreciation.

A higher Sharpe-like ratio suggests better value, but unexpected costs, and challenges its reliability.

Does not capture non-quantifiable factors like comfort, style, or overall satisfaction.

Sharpe struggles with occasional extreme gains or losses, penalising volatility in both directions — even positive volatility that drives strong returns.

It assumes returns follow a normal distribution — rarely true in real markets.

• Fund X:?Stable returns with low volatility may score highly on the Sharpe Ratio, yet offer limited growth potential.
• Fund Y:?Occasional extreme gains with periods of losses may appear riskier, despite delivering stronger long-term performance.

Asymmetric return: An uneven distribution of gains and losses.

Symmetrical: return pattern, is evenly distributed around an average.

Asymmetric returns are characterised by occasional large gains or losses that significantly deviate from the average.

• Fund X: a more symmetrical pattern.
• Fund Y: with occasionally extreme gains — an asymmetric pattern.

Sharpe struggles with asymmetric because it penalises both positive and negative equally, even though investors welcome large positive returns.

This is why the?Sortino Ratio, which only considers downside risk, is better for investments with asymmetric return profiles.

But how many IFA’s or wealth managers use it?

Relying Solely on Benchmark Returns — Buying a Car, a Fund, or a Multi-Asset Strategy

• Limited Perspective: Benchmarks can help in specific areas but miss individual needs. A top-rated car may lack space or speed, just as a strong-performing fund may exceed risk tolerance or fail income goals. Likewise, a benchmark multi-asset strategy may overlook asset behaviour in varied conditions. Correlation is not always correlation.
• Risk Considerations:?A manager may outperform a benchmark by taking excessive risks, just as a highly-rated car may have high maintenance costs. In both funds and multi-asset strategies, this can mean overexposure to riskier assets. And unseen doubling up. Owning the same underlying assets (risk) in apparently different investments.
• Style Bias:?Managers have distinct investment styles, like cars designed for off-road, family, or sport. Comparing them solely on benchmarks ignores their approach. They also often change style.
• Short-Term Focus:?Benchmarks fluctuate, and judging a manager on short-term outperformance may be misleading. Similarly, trends in cars may not reflect long-term reliability. Think of EV's.
• Survivorship Bias:?Changing benchmarks distorts results. Just as automotive technology evolves, relying on outdated benchmarks may lead to poor decisions.
• Manager Skill vs. Market Conditions:?A manager’s success may stem from favorable markets rather than skill. Similarly, a car's popularity may rely on brand perception rather than performance.
• Consistency in Outperformance:?Sustained success matters more than short-term spikes. Overemphasis on benchmarks obscure this.
• Qualitative Factors: Benchmark data may overlook critical factors like investment philosophy, expertise, and process.

Law of Small Numbers - the mistaken belief that small samples reflect broader trends.

Gambler’s Fallacy:?Believing that after declines, a rebound is "due" is a common trap.

Bias of Confidence Over Doubt:?Overconfidence in limited data misleads decisions.

The Halo Effect:?Celebrity fund managers or financial advisers endorsements can overshadow objective evaluation.

The Raven Paradox: (Confirmation Bias)

• "All ravens are black."? but rare albino ravens exist.
• Misleading assumptions stem from this flawed logic.

Correlation vs. Causation

• Correlation shows how assets move relative to each other but does?not?imply causation.
• Historic correlations can break down, especially in volatile markets.

Diversification: Not a Free Lunch

• Too much diversification dilutes gains.
• More assets can mean higher fees and complexity. Hidden costs.
• In crises, even uncorrelated assets can start moving together.
• Diversifying into multiple investments can create unseen risks ( uncorrelated assets can start moving together ) if underlying exposures are poorly understood or monitored. Hidden risk.

Compounding (if I read that word again, I'll go mad): Not Always a Free Lunch Either

·?Time Dependency:?Requires consistency. Missing key growth periods drastically reduces potential gains.

·?Costs and Fees: Charges such as fund fees or transaction costs erode compounding.

·?Emotion:?Panic and selling during downturns interrupts compounding process.

THE ANSWER IS 42

The Hitchhiker's Guide to the Galaxy

After seven and a half million years of calculation, the computer produces the answer:?42?— but no one knows what the actual question was.

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