Embedded Files
An analytics approach to debiasing asset - management decisions
Investment managers are pursuing analytics-aided improvements in many different business areas, from customer and asset acquisition to operations and overhead costs. The change
area we focus on in this discussion is investment performance improvement, specifically the debiasing of investment decisions. With the help of more advanced analytics than they are already using, funds have been able to measure the role played by bias in suboptimal trading decisions, connecting particular biases
to particular decisions. Such discoveries
provide the necessary foundation for effective countermeasures—the debiasing methods that can bring significant performance improvements to a pressured industry.
read more
Understanding K-means Clustering in Machine Learning
K-means clustering is one of the simplest and popular unsupervised machine learning algorithms.
Typically, unsupervised algorithms make inferences from datasets using only input vectors without referring to known or labeled outcomes.
A cluster refers to a collection of data points aggregated together because of certain similarities.
You’ll define a target number k, which refers to the number of centroids you need in the dataset. A centroid is the imaginary or real location representing the center of the cluster.
Every data point is allocated to each of the clusters through reducing the in-cluster sum of squares. In other words, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible. The ‘means’ in the K-means refers to averaging of the data; that is, finding the centroid.
read more
Supply-chain talent management has moved to front-of-mind in recent years, as borne out by surveys and recent events. The supply-chain dysfunctions seen in the last two years are largely a thing of the past, but they created a high-stress environment, marked by high turnover and a search for talent.
One consequence of this is that methods and practices developed over many years, both in management techniques and using software tools, were lost. Supply chain, in particular, relies on institutional knowledge that is difficult to capture in standards and guidelines. There is no common, agreed set of procedures or policies, as it is very much dependent on the context of each company and industry. Even if a company were disciplined enough to try to capture and train staff on in-house processes, there are gaps in knowledge atthe best of times..
read more
One consequence of this is that methods and practices developed over many years, both in management techniques and using software tools, were lost. Supply chain, in particular, relies on institutional knowledge that is difficult to capture in standards and guidelines. There is no common, agreed set of procedures or policies, as it is very much dependent on the context of each company and industry. Even if a company were disciplined enough to try to capture and train staff on in-house processes, there are gaps in knowledge atthe best of times..
read more
Impact on the Retail Industry -
A large language model can be used to analyze customer data, such as past purchases, browsing history, and social media activity, to identify patterns and trends. This information can then be used to generate personalized recommendations for products and services. For example, an LLM could be used to recommend products to customers based on their interests, needs, and budget.Impact on the Retail Industry -
- Answering Customer Queries
- Asisting with Purchases
- Fraud Detection
read more
Page updated
Google Sites
Report abuse