{"id":13424,"date":"2020-10-19T09:00:01","date_gmt":"2020-10-19T07:00:01","guid":{"rendered":"https:\/\/my-life.lu\/?p=13424"},"modified":"2025-05-12T11:08:20","modified_gmt":"2025-05-12T09:08:20","slug":"investments-dont-confuse-cause-and-effect-30067","status":"publish","type":"post","link":"https:\/\/my-life.lu\/en\/investments-dont-confuse-cause-and-effect-30067\/","title":{"rendered":"Investments: don\u2019t confuse cause and effect"},"content":{"rendered":"

Are you planning to go it alone in the investment world? If so, there\u2019s one key fact you need to know. This essential statistical rule will help you become that worldly-wise investor who always takes the time to assess the quality of the data presented and knows how to verify their sources with intellectual rigour.<\/p>\n

In the myLIFE series on behavioural finance<\/strong><\/span><\/a>, we often try to alert you to the various forms of cognitive bias that can cloud your judgement and lead you to make the wrong investment decisions. In light of this bias, we regularly highlight just how useful professional guidance can be. But there\u2019s nothing to stop you from going it alone.<\/p>\n

If this is your chosen path, you\u2019re sure to be on the lookout for reliable indicators on which to base your decision-making. Seeking to assess the potential performance of an investment means engaging in a process of factual analysis, based on figures and, most likely, statistics.<\/p>\n

Looking for indicators to guide your decisions is definitely a good move, so long as they are based on reliable and relevant data. Just as a recipe that looks good on paper will lead to a bad dish if you use poor-quality ingredients, good statistical analysis cannot produce reliable results if it\u2019s fed inaccurate or falsified data.<\/p>\n

\"\"<\/p>\n

Statistical analysis cannot produce reliable results if it draws on inaccurate or falsified data.<\/p><\/blockquote>\n

\u201cGarbage in, garbage out!\u201d<\/h2>\n

There\u2019s a lot of truth in the phrase \u201cgarbage in, garbage out\u201d. It\u2019s definitely the case that if you feed bad data into a calculation system, you\u2019ll never end up with reliable indicators. It\u2019s vital to understand that if there is any \u201cnoise\u201d affecting the quality of the data, it\u2019s almost certain that any lessons you draw from your analysis will be less than crystal clear.<\/p>\n

Let\u2019s imagine an investor sitting down to look for financial information to guide his investment decisions, as he does every morning. He visits all of his favourite media outlets, reads his emails and checks his professional and personal social media feeds. As he reads, he finds out that scientists are suggesting that there could be a link between eating a particular food and a decline in a given disease. His curiosity is piqued, but he\u2019s not about to waste time. \u201cHow can I use this information to invest?\u201d he muses. At this stage, there is no proof of the link in question, and in any case the details of the study are not reported.<\/p>\n

A short time later, he reads the latest post by an influential blogger who shares an eye-catching graph: for the last decade, the performance of a stock exchange index he has access to has followed an astonishingly similar path to that of banana production in Brazil. Our investor thinks he\u2019s onto a winner. Why do any more research when the facts are as clear as day?<\/p>\n

Do you see the link between these two examples? The two situations are very similar when you stop to think about it. In both instances, the investor would do well to set his reading preferences aside and take the time to dig deeper and understand what could be hidden behind the information. There is obviously a likely correlation between the two variables in both cases, but that doesn\u2019t necessarily mean that there\u2019s a causal link. And in the world of statistics, this distinction between correlation and causality changes everything.<\/p>\n

Unlike a cause, a correlation is simply a historical, temporary measure of two different things that may not actually be linked. There\u2019s nothing to show that this statistic won\u2019t change over time or that it will hold true in future. In investment, there are bad actors out there who like nothing better than unearthing correlations over insignificant periods to cloud the judgement of the investor.<\/p>\n

What\u2019s the moral of the story? When you hear about a promising investment strategy, always make sure that it is based on an actual relationship of cause and effect rather than mere correlation.<\/p>\n

Remember the golden rule of statistics: correlation is not causality.<\/p><\/blockquote>\n

Correlation is not causality<\/h2>\n

Never forget the golden rule of statistical analysis: correlation is not the same as causality. Just because two variables move in almost exactly the same way over time, that doesn\u2019t mean that one change is causing the other.<\/p>\n

Before you make an investment decision, always make sure that the information you\u2019re shown is not what are sometimes referred to as \u201cspurious correlations\u201d.<\/p>\n

In statistics, a spurious correlation is a link between two variables that appears causal but isn\u2019t. This false correlation is often caused by a third element called a \u201cconfounding factor\u201d that isn\u2019t apparent when you assess the information presented.<\/p>\n

Our brains love simple narratives and delight in drawing easy-to-understand associations, even where they don\u2019t exist. Jumping to conclusions is what our brains do best, which is why it\u2019s so easy to believe, when we look at a graph, that a movement in variable A is linked to a movement in variable B, or vice versa. If we dig into the statistics a little more, though, it may become clear that such a movement on a graph is a simple coincidence rather than cause and effect.<\/p>\n

To help make this a little clearer, here\u2019s an example from everyday life that\u2019s credible, recurrent and easy to debunk. It\u2019s a classic trope in summer to look at the relationship and show the correlation \u2013 generally quite a high correlation \u2013 between the number of ice creams sold and the death rate. But we all know that ice creams aren\u2019t deadly. In this example, if it\u2019s a particularly hot summer, it is logical that everyone will want to eat more ice creams and that people in a fragile state of health will sadly stand a greater chance of passing away in the heat.<\/p>\n

Here, the confounding factor is undoubtedly the heat and we are bound to be able to show a correlation by finding the exact number of days of hot weather and the precise temperature beyond which both ice cream sales and deaths increase.<\/p>\n

Consider yourself warned! If you would like to find out more about spurious correlations by looking at a few examples, you might be interested in our next article: \u201cAssessing performance: why not everything is as it seems!<\/strong><\/span><\/a>\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

Are you planning to go it alone in the investment world? If so, there\u2019s one key fact you need to know. This essential statistical rule will help you become that worldly-wise investor who always takes the time to assess the quality of the data presented and knows how to verify their sources with intellectual rigour. … <\/p>\n

Continue reading “Investments: don\u2019t confuse cause and effect”<\/span><\/a><\/p>\n","protected":false},"author":11,"featured_media":13416,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":"","beyondwords_generate_audio":"1","beyondwords_integration_method":"","beyondwords_project_id":"44704","beyondwords_content_id":"18fd9bb2-b545-4f83-ba1c-1dc1a779da35","beyondwords_preview_token":"20c7041b-2b93-4e1a-9b2e-8d6fa3bce221","beyondwords_player_content":"","beyondwords_player_style":"standard","beyondwords_language_code":"en_GB","beyondwords_language_id":"","beyondwords_title_voice_id":"775","beyondwords_body_voice_id":"775","beyondwords_summary_voice_id":"775","beyondwords_error_message":"","beyondwords_disabled":"","beyondwords_delete_content":"","beyondwords_podcast_id":"","beyondwords_hash":"","publish_post_to_speechkit":"","speechkit_hash":"","speechkit_generate_audio":"","speechkit_project_id":"","speechkit_podcast_id":"","speechkit_error_message":"","speechkit_disabled":"","speechkit_access_key":"","speechkit_error":"","speechkit_info":"","speechkit_response":"","speechkit_retries":"","speechkit_status":"","speechkit_updated_at":"","_speechkit_link":"","_speechkit_text":""},"categories":[188,15],"tags":[400,586],"class_list":["post-13424","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-investment-en","category-myinvest-en","tag-investment","tag-profil-dinvestisseur-en","my_invest"],"yoast_head":"\nInvestments: don\u2019t confuse cause and effect - 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