Sökning: "Walk Forward Analysis"

Visar resultat 1 - 5 av 11 uppsatser innehållade orden Walk Forward Analysis.

  1. 1. Analysis of condition for ALD deposition of ferroelectric HZO

    Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

    Författare :Teodor Qvint; [2022]
    Nyckelord :HZO; Ferro; ALD; Temperature; Purge time; Technology and Engineering;

    Sammanfattning : Deposition of ferroelectric hafnium zirconium oxide (HZO) on semiconductor samples with Atomic Layer Deposition (ALD) has proven to be a viable method of production. But while the physical processes of ALD deposition is relatively well know, there exists some gaps in knowledge about different parameters for the ALD and the resulting depositions. LÄS MER

  2. 2. Hierarchical Clustering in Risk-Based Portfolio Construction

    Master-uppsats, KTH/Matematisk statistik

    Författare :Natasha Nanakorn; Elin Palmgren; [2021]
    Nyckelord :Portfolio construction; asset allocation; risk-based asset allocation; hierarchical clustering; agglomerative clustering; hierarchical risk parity; risk; volatility; Portföljallokering; portföljhantering; portföljmetoder; riskbaserad portföljallokering; hierarkisk klustring; agglomerativ klustring; risk; volatilitet;

    Sammanfattning : Following the global financial crisis, both risk-based and heuristic portfolio construction methods have received much attention from both academics and practitioners since these methods do not rely on the estimation of expected returns and as such are assumed to be more stable than Markowitz's traditional mean-variance portfolio. In 2016, Lopéz de Prado presented the Hierarchical Risk Parity (HRP), a new approach to portfolio construction which combines hierarchical clustering of assets with a heuristic risk-based allocation strategy in order to increase stability and improve out-of-sample performance. LÄS MER

  3. 3. Smile! It increases your face value

    Magister-uppsats, Lunds universitet/Nationalekonomiska institutionen

    Författare :Anton Evilevitch; Dennis Elgegren; [2019]
    Nyckelord :Implied Volatility Surface; Rules of Thumb; European Option Contracts; Walk Forward Analysis; Stepwise Procedure; Business and Economics;

    Sammanfattning : This thesis examines some of the multiple variations of the previously established Rules of Thumb; which are used in attempting to explain implied volatility sur- faces. Here, these Rules are extensively tested on the Swedish stock market index (OMXS30) using rolling window analysis and linear stepwise regressions with for- ward selection. LÄS MER

  4. 4. A Snapshot of Climate Change Adaptation Efforts in American Urban Planning & Development

    Master-uppsats, Lunds universitet/Avdelningen för Riskhantering och Samhällssäkerhet

    Författare :Eliisa Carter; [2019]
    Nyckelord :climate change adaptation; urban development; urban planning; land use planning; United States of America; risk reduction; sustainable design; hazard mitigation; Technology and Engineering;

    Sammanfattning : To understand how American cities “walk the talk” regarding climate change adaptation, the question “What effect do American city-wide adaptation efforts have on urban development trends?” is posed. The method covers the breadth and depth of the subject by studying the overall national climate discourse and utilizing three major American cities as case studies. LÄS MER

  5. 5. High-variance multivariate time series forecasting using machine learning

    Master-uppsats, Uppsala universitet/Institutionen för informatik och media

    Författare :Nikola Katardjiev; [2018]
    Nyckelord :Data science; alcohol abuse; time series; forecastin; machine learning; deep learning; neural networks; regression; Data science; alkoholmissbruk; tidsserieanalys; prognos; maskininlärning; deep learning; neurala nätverk; regression;

    Sammanfattning : There are several tools and models found in machine learning that can be used to forecast a certain time series; however, it is not always clear which model is appropriate for selection, as different models are suited for different types of data, and domain-specific transformations and considerations are usually required. This research aims to examine the issue by modeling four types of machine- and deep learning algorithms - support vector machine, random forest, feed-forward neural network, and a LSTM neural network - on a high-variance, multivariate time series to forecast trend changes one time step in the future, accounting for lag. LÄS MER